Overview

Dataset statistics

Number of variables64
Number of observations607464
Missing cells9152173
Missing cells (%)23.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory296.6 MiB
Average record size in memory512.0 B

Variable types

Numeric20
Categorical41
Unsupported2
Boolean1

Alerts

EXONERADO has constant value "False"Constant
OFA_ESTADO has constant value "1.0"Constant
ASA_EXONERA has constant value "0.0"Constant
ASA_BECA has constant value "0.0"Constant
ASA_ESTADO_DDA has constant value "1.0"Constant
USU_NACIONALIDAD has a high cardinality: 75 distinct valuesHigh cardinality
PARROQUIA_RESIDE has a high cardinality: 1221 distinct valuesHigh cardinality
CANTON_RESIDE has a high cardinality: 229 distinct valuesHigh cardinality
IES_SIGLAS_INSTIT has a high cardinality: 67 distinct valuesHigh cardinality
NOMBRE_INSTITUCION has a high cardinality: 399 distinct valuesHigh cardinality
CAMPUS_NOMBRE has a high cardinality: 184 distinct valuesHigh cardinality
CAMPUS_CIUDAD has a high cardinality: 100 distinct valuesHigh cardinality
CANTON has a high cardinality: 83 distinct valuesHigh cardinality
PARROQUIA has a high cardinality: 127 distinct valuesHigh cardinality
CARRERA has a high cardinality: 740 distinct valuesHigh cardinality
ASA_OBSERVACION has a high cardinality: 3418 distinct valuesHigh cardinality
Unnamed: 0 is highly overall correlated with COD_PARROQUIA_RESIDE and 3 other fieldsHigh correlation
ASA_ID is highly overall correlated with INI_ID and 9 other fieldsHigh correlation
INI_ID is highly overall correlated with ASA_ID and 10 other fieldsHigh correlation
INS_ID is highly overall correlated with ASA_ID and 10 other fieldsHigh correlation
COD_PARROQUIA_RESIDE is highly overall correlated with Unnamed: 0 and 6 other fieldsHigh correlation
COD_CANTON_RESIDE is highly overall correlated with Unnamed: 0 and 6 other fieldsHigh correlation
COD_PROV_RESIDE is highly overall correlated with Unnamed: 0 and 6 other fieldsHigh correlation
PRD_ID is highly overall correlated with SEGMENTACION_PERSONA and 4 other fieldsHigh correlation
POS_ID is highly overall correlated with ASA_ID and 12 other fieldsHigh correlation
IES_ID is highly overall correlated with IES_SIGLAS_INSTIT and 4 other fieldsHigh correlation
CAM_ID is highly overall correlated with IES_SIGLAS_INSTIT and 5 other fieldsHigh correlation
CCP_ID is highly overall correlated with ASA_ID and 10 other fieldsHigh correlation
CUS_ID_PADRE is highly overall correlated with ASA_ID and 12 other fieldsHigh correlation
CUS_ID_HIJO is highly overall correlated with ASA_ID and 12 other fieldsHigh correlation
CAR_ID is highly overall correlated with TIPO_INSTITUCION and 1 other fieldsHigh correlation
OFA_ID is highly overall correlated with ASA_ID and 10 other fieldsHigh correlation
PRD_ID_NUM_POSTULACION is highly overall correlated with PRD_ID_NUM_ASIGNACION and 5 other fieldsHigh correlation
PRD_ID_NUM_ASIGNACION is highly overall correlated with PRD_ID_NUM_POSTULACION and 5 other fieldsHigh correlation
PROVINCIA_RESIDE is highly overall correlated with Unnamed: 0 and 6 other fieldsHigh correlation
SEGMENTACION_PERSONA is highly overall correlated with PRD_ID and 6 other fieldsHigh correlation
SEGMETO_CARRERA is highly overall correlated with PRD_ID and 10 other fieldsHigh correlation
IES_SIGLAS_INSTIT is highly overall correlated with IES_ID and 9 other fieldsHigh correlation
TIPO_INSTITUCION is highly overall correlated with IES_ID and 7 other fieldsHigh correlation
TIPO_FINANCIAMIENTO is highly overall correlated with INI_ID and 14 other fieldsHigh correlation
CAMPUS_CIUDAD is highly overall correlated with COD_PARROQUIA_RESIDE and 11 other fieldsHigh correlation
PROVINCIA is highly overall correlated with COD_PARROQUIA_RESIDE and 7 other fieldsHigh correlation
CANTON is highly overall correlated with COD_PARROQUIA_RESIDE and 11 other fieldsHigh correlation
AREA is highly overall correlated with SUBAREAHigh correlation
SUBAREA is highly overall correlated with CAR_ID and 1 other fieldsHigh correlation
MODALIDAD is highly overall correlated with JORNADAHigh correlation
NIVEL is highly overall correlated with IES_SIGLAS_INSTIT and 2 other fieldsHigh correlation
JORNADA is highly overall correlated with IES_SIGLAS_INSTIT and 3 other fieldsHigh correlation
ACEPTA_CUPO is highly overall correlated with ACEPTA_CUPO_DDAHigh correlation
ASA_ESTADO is highly overall correlated with ASA_ID and 3 other fieldsHigh correlation
PER_ID is highly overall correlated with ASA_ID and 14 other fieldsHigh correlation
SEGMENTO is highly overall correlated with PRD_ID and 10 other fieldsHigh correlation
TIPO_CUPO is highly overall correlated with INI_ID and 20 other fieldsHigh correlation
INSTANCIA_POSTULACION is highly overall correlated with PRD_ID and 13 other fieldsHigh correlation
INSTANCIA_ASIGNACION is highly overall correlated with PRD_ID and 13 other fieldsHigh correlation
ASA_GRATUIDAD is highly overall correlated with ASA_ESTADO and 1 other fieldsHigh correlation
GRATUIDAD is highly overall correlated with ASA_ESTADO and 1 other fieldsHigh correlation
ACEPTA_CUPO_DDA is highly overall correlated with ACEPTA_CUPO and 1 other fieldsHigh correlation
archivo is highly overall correlated with ASA_ID and 14 other fieldsHigh correlation
USU_NACIONALIDAD is highly imbalanced (98.8%)Imbalance
ETNIA is highly imbalanced (54.0%)Imbalance
SEGMENTACION_PERSONA is highly imbalanced (69.6%)Imbalance
SEGMETO_CARRERA is highly imbalanced (72.8%)Imbalance
MODALIDAD is highly imbalanced (58.1%)Imbalance
NIVEL is highly imbalanced (61.0%)Imbalance
ASA_ESTADO is highly imbalanced (99.9%)Imbalance
SEGMENTO is highly imbalanced (63.3%)Imbalance
DISCAPACIDAD is highly imbalanced (93.2%)Imbalance
INSTANCIA_POSTULACION is highly imbalanced (56.3%)Imbalance
ASA_OBSERVACION is highly imbalanced (67.6%)Imbalance
ASA_GRATUIDAD is highly imbalanced (83.2%)Imbalance
GRATUIDAD is highly imbalanced (88.4%)Imbalance
ACEPTA_CUPO_DDA is highly imbalanced (51.2%)Imbalance
ASA_ID has 246733 (40.6%) missing valuesMissing
INI_ID has 134696 (22.2%) missing valuesMissing
INS_ID has 134696 (22.2%) missing valuesMissing
GENERO has 138931 (22.9%) missing valuesMissing
USU_FECHA_NAC has 134721 (22.2%) missing valuesMissing
USU_NACIONALIDAD has 138459 (22.8%) missing valuesMissing
ETNIA has 149542 (24.6%) missing valuesMissing
COD_PARROQUIA_RESIDE has 165315 (27.2%) missing valuesMissing
PARROQUIA_RESIDE has 165315 (27.2%) missing valuesMissing
COD_CANTON_RESIDE has 165315 (27.2%) missing valuesMissing
CANTON_RESIDE has 165315 (27.2%) missing valuesMissing
COD_PROV_RESIDE has 165315 (27.2%) missing valuesMissing
PROVINCIA_RESIDE has 165315 (27.2%) missing valuesMissing
PRD_ID has 134696 (22.2%) missing valuesMissing
SEGMENTACION_PERSONA has 134696 (22.2%) missing valuesMissing
POS_ID has 134696 (22.2%) missing valuesMissing
POS_NOTA has 134696 (22.2%) missing valuesMissing
POS_PRIORIDAD has 134696 (22.2%) missing valuesMissing
SEGMETO_CARRERA has 134696 (22.2%) missing valuesMissing
IES_ID has 134696 (22.2%) missing valuesMissing
IES_SIGLAS_INSTIT has 217088 (35.7%) missing valuesMissing
NOMBRE_INSTITUCION has 134696 (22.2%) missing valuesMissing
TIPO_INSTITUCION has 134696 (22.2%) missing valuesMissing
TIPO_FINANCIAMIENTO has 134696 (22.2%) missing valuesMissing
CAMPUS_NOMBRE has 134696 (22.2%) missing valuesMissing
CAM_ID has 134696 (22.2%) missing valuesMissing
CAMPUS_CIUDAD has 134696 (22.2%) missing valuesMissing
PROVINCIA has 134696 (22.2%) missing valuesMissing
CANTON has 134696 (22.2%) missing valuesMissing
PARROQUIA has 134696 (22.2%) missing valuesMissing
CCP_ID has 134696 (22.2%) missing valuesMissing
CUS_ID_PADRE has 134696 (22.2%) missing valuesMissing
CUS_ID_HIJO has 134696 (22.2%) missing valuesMissing
CAR_ID has 134696 (22.2%) missing valuesMissing
CARRERA has 134696 (22.2%) missing valuesMissing
AREA has 134696 (22.2%) missing valuesMissing
SUBAREA has 134696 (22.2%) missing valuesMissing
MODALIDAD has 134696 (22.2%) missing valuesMissing
NIVEL has 134696 (22.2%) missing valuesMissing
JORNADA has 134696 (22.2%) missing valuesMissing
ACEPTA_CUPO has 134696 (22.2%) missing valuesMissing
ASA_ESTADO has 134696 (22.2%) missing valuesMissing
OFA_ID has 134696 (22.2%) missing valuesMissing
ASA_FECHA_ACEPTACION has 214686 (35.3%) missing valuesMissing
EXONERADO has 134696 (22.2%) missing valuesMissing
OFA_ESTADO has 134696 (22.2%) missing valuesMissing
ASA_EXONERA has 134696 (22.2%) missing valuesMissing
PER_ID has 134696 (22.2%) missing valuesMissing
SEGMENTO has 134696 (22.2%) missing valuesMissing
TIPO_CUPO has 134696 (22.2%) missing valuesMissing
DISCAPACIDAD has 134696 (22.2%) missing valuesMissing
ASA_BECA has 134696 (22.2%) missing valuesMissing
PRD_ID_NUM_POSTULACION has 134696 (22.2%) missing valuesMissing
PRD_ID_NUM_ASIGNACION has 134696 (22.2%) missing valuesMissing
INSTANCIA_POSTULACION has 134696 (22.2%) missing valuesMissing
INSTANCIA_ASIGNACION has 134696 (22.2%) missing valuesMissing
ASA_OBSERVACION has 134696 (22.2%) missing valuesMissing
ASA_GRATUIDAD has 246733 (40.6%) missing valuesMissing
GRATUIDAD has 253300 (41.7%) missing valuesMissing
ACEPTA_CUPO_DDA has 246733 (40.6%) missing valuesMissing
ASA_ESTADO_DDA has 246733 (40.6%) missing valuesMissing
USU_FECHA_NAC is an unsupported type, check if it needs cleaning or further analysisUnsupported
ASA_FECHA_ACEPTACION is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-03-10 07:46:29.141610
Analysis finished2023-03-10 07:48:35.444489
Duration2 minutes and 6.3 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

Distinct134696
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61302.88
Minimum1
Maximum134696
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2023-03-10T02:48:35.485638image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6075
Q130374
median60747
Q391120
95-th percentile120026
Maximum134696
Range134695
Interquartile range (IQR)60746

Descriptive statistics

Standard deviation36006.914
Coefficient of variation (CV)0.58736088
Kurtosis-1.0924354
Mean61302.88
Median Absolute Deviation (MAD)30373
Skewness0.083618822
Sum3.7239293 × 1010
Variance1.2964978 × 109
MonotonicityNot monotonic
2023-03-10T02:48:35.551120image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 5
 
< 0.1%
69982 5
 
< 0.1%
69992 5
 
< 0.1%
69991 5
 
< 0.1%
69990 5
 
< 0.1%
69989 5
 
< 0.1%
69988 5
 
< 0.1%
69987 5
 
< 0.1%
69986 5
 
< 0.1%
69985 5
 
< 0.1%
Other values (134686) 607414
> 99.9%
ValueCountFrequency (%)
1 5
< 0.1%
2 5
< 0.1%
3 5
< 0.1%
4 5
< 0.1%
5 5
< 0.1%
6 5
< 0.1%
7 5
< 0.1%
8 5
< 0.1%
9 5
< 0.1%
10 5
< 0.1%
ValueCountFrequency (%)
134696 1
< 0.1%
134695 1
< 0.1%
134694 1
< 0.1%
134693 1
< 0.1%
134692 1
< 0.1%
134691 1
< 0.1%
134690 1
< 0.1%
134689 1
< 0.1%
134688 1
< 0.1%
134687 1
< 0.1%

ASA_ID
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct360731
Distinct (%)100.0%
Missing246733
Missing (%)40.6%
Infinite0
Infinite (%)0.0%
Mean2097049.3
Minimum1869105
Maximum2301886
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2023-03-10T02:48:35.617294image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1869105
5-th percentile1887141.5
Q11959287.5
median2121521
Q32211703.5
95-th percentile2283849.5
Maximum2301886
Range432781
Interquartile range (IQR)252416

Descriptive statistics

Standard deviation133557.93
Coefficient of variation (CV)0.0636885
Kurtosis-1.3219474
Mean2097049.3
Median Absolute Deviation (MAD)122520
Skewness-0.23765094
Sum7.564707 × 1011
Variance1.783772 × 1010
MonotonicityNot monotonic
2023-03-10T02:48:35.674563image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1915822 1
 
< 0.1%
1922936 1
 
< 0.1%
1972000 1
 
< 0.1%
1977549 1
 
< 0.1%
1874763 1
 
< 0.1%
1893071 1
 
< 0.1%
1958066 1
 
< 0.1%
1924116 1
 
< 0.1%
1962877 1
 
< 0.1%
1973664 1
 
< 0.1%
Other values (360721) 360721
59.4%
(Missing) 246733
40.6%
ValueCountFrequency (%)
1869105 1
< 0.1%
1869106 1
< 0.1%
1869107 1
< 0.1%
1869108 1
< 0.1%
1869109 1
< 0.1%
1869110 1
< 0.1%
1869111 1
< 0.1%
1869112 1
< 0.1%
1869113 1
< 0.1%
1869114 1
< 0.1%
ValueCountFrequency (%)
2301886 1
< 0.1%
2301885 1
< 0.1%
2301884 1
< 0.1%
2301883 1
< 0.1%
2301882 1
< 0.1%
2301881 1
< 0.1%
2301880 1
< 0.1%
2301879 1
< 0.1%
2301878 1
< 0.1%
2301877 1
< 0.1%

INI_ID
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct450555
Distinct (%)95.3%
Missing134696
Missing (%)22.2%
Infinite0
Infinite (%)0.0%
Mean5837607.1
Minimum3941098
Maximum7723589
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2023-03-10T02:48:35.732785image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum3941098
5-th percentile4003834.8
Q15450172
median6114654
Q36842112.2
95-th percentile7082826.5
Maximum7723589
Range3782491
Interquartile range (IQR)1391940.2

Descriptive statistics

Standard deviation1072802.8
Coefficient of variation (CV)0.1837744
Kurtosis-0.96148936
Mean5837607.1
Median Absolute Deviation (MAD)714774
Skewness-0.44749766
Sum2.7598338 × 1012
Variance1.1509058 × 1012
MonotonicityNot monotonic
2023-03-10T02:48:35.791137image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7038943 6
 
< 0.1%
4330233 5
 
< 0.1%
7084088 5
 
< 0.1%
4090083 5
 
< 0.1%
4065674 5
 
< 0.1%
6883629 5
 
< 0.1%
6788754 5
 
< 0.1%
4040747 5
 
< 0.1%
6882194 5
 
< 0.1%
5477111 5
 
< 0.1%
Other values (450545) 472717
77.8%
(Missing) 134696
 
22.2%
ValueCountFrequency (%)
3941098 1
< 0.1%
3941099 1
< 0.1%
3941101 1
< 0.1%
3941104 1
< 0.1%
3941106 1
< 0.1%
3941107 1
< 0.1%
3941109 2
< 0.1%
3941110 1
< 0.1%
3941111 1
< 0.1%
3941112 1
< 0.1%
ValueCountFrequency (%)
7723589 1
< 0.1%
7723585 1
< 0.1%
7723567 1
< 0.1%
7723560 1
< 0.1%
7723558 1
< 0.1%
7723547 1
< 0.1%
7723545 1
< 0.1%
7723543 1
< 0.1%
7723542 1
< 0.1%
7723541 1
< 0.1%

INS_ID
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct450555
Distinct (%)95.3%
Missing134696
Missing (%)22.2%
Infinite0
Infinite (%)0.0%
Mean9812750
Minimum7006528
Maximum12265994
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2023-03-10T02:48:35.852033image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum7006528
5-th percentile7132016.6
Q19104725
median10003194
Q311439064
95-th percentile11920521
Maximum12265994
Range5259466
Interquartile range (IQR)2334338.5

Descriptive statistics

Standard deviation1611931.4
Coefficient of variation (CV)0.16426907
Kurtosis-1.1014073
Mean9812750
Median Absolute Deviation (MAD)1410499
Skewness-0.32736817
Sum4.6391542 × 1012
Variance2.5983227 × 1012
MonotonicityNot monotonic
2023-03-10T02:48:35.912248image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11832713 6
 
< 0.1%
7692444 5
 
< 0.1%
11923035 5
 
< 0.1%
7304515 5
 
< 0.1%
7255675 5
 
< 0.1%
11522096 5
 
< 0.1%
11360649 5
 
< 0.1%
7205809 5
 
< 0.1%
11519216 5
 
< 0.1%
9161186 5
 
< 0.1%
Other values (450545) 472717
77.8%
(Missing) 134696
 
22.2%
ValueCountFrequency (%)
7006528 1
< 0.1%
7006530 1
< 0.1%
7006534 1
< 0.1%
7006540 1
< 0.1%
7006544 1
< 0.1%
7006546 1
< 0.1%
7006550 2
< 0.1%
7006552 1
< 0.1%
7006554 1
< 0.1%
7006556 1
< 0.1%
ValueCountFrequency (%)
12265994 1
< 0.1%
12265981 1
< 0.1%
12265974 1
< 0.1%
12265960 1
< 0.1%
12265959 1
< 0.1%
12265958 1
< 0.1%
12265953 1
< 0.1%
12265952 1
< 0.1%
12265948 1
< 0.1%
12265947 1
< 0.1%

GENERO
Categorical

Distinct2
Distinct (%)< 0.1%
Missing138931
Missing (%)22.9%
Memory size4.6 MiB
FEMENINO
263617 
MASCULINO
204916 

Length

Max length9
Median length8
Mean length8.4373566
Min length8

Characters and Unicode

Total characters3953180
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFEMENINO
2nd rowMASCULINO
3rd rowFEMENINO
4th rowFEMENINO
5th rowMASCULINO

Common Values

ValueCountFrequency (%)
FEMENINO 263617
43.4%
MASCULINO 204916
33.7%
(Missing) 138931
22.9%

Length

2023-03-10T02:48:35.963693image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-10T02:48:36.007311image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
femenino 263617
56.3%
masculino 204916
43.7%

Most occurring characters

ValueCountFrequency (%)
N 732150
18.5%
E 527234
13.3%
M 468533
11.9%
I 468533
11.9%
O 468533
11.9%
F 263617
 
6.7%
A 204916
 
5.2%
S 204916
 
5.2%
C 204916
 
5.2%
U 204916
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 3953180
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 732150
18.5%
E 527234
13.3%
M 468533
11.9%
I 468533
11.9%
O 468533
11.9%
F 263617
 
6.7%
A 204916
 
5.2%
S 204916
 
5.2%
C 204916
 
5.2%
U 204916
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 3953180
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 732150
18.5%
E 527234
13.3%
M 468533
11.9%
I 468533
11.9%
O 468533
11.9%
F 263617
 
6.7%
A 204916
 
5.2%
S 204916
 
5.2%
C 204916
 
5.2%
U 204916
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3953180
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 732150
18.5%
E 527234
13.3%
M 468533
11.9%
I 468533
11.9%
O 468533
11.9%
F 263617
 
6.7%
A 204916
 
5.2%
S 204916
 
5.2%
C 204916
 
5.2%
U 204916
 
5.2%

USU_FECHA_NAC
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing134721
Missing (%)22.2%
Memory size4.6 MiB

USU_NACIONALIDAD
Categorical

HIGH CARDINALITY  IMBALANCE  MISSING 

Distinct75
Distinct (%)< 0.1%
Missing138459
Missing (%)22.8%
Memory size4.6 MiB
ECUATORIANA
465883 
ECUADOR
 
1159
107
 
737
COLOMBIANA
 
378
VENEZOLANA
 
285
Other values (70)
 
563

Length

Max length24
Median length11
Mean length10.973868
Min length2

Characters and Unicode

Total characters5146799
Distinct characters40
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)< 0.1%

Sample

1st rowECUATORIANA
2nd rowECUATORIANA
3rd rowECUATORIANA
4th rowECUATORIANA
5th rowECUATORIANA

Common Values

ValueCountFrequency (%)
ECUATORIANA 465883
76.7%
ECUADOR 1159
 
0.2%
107 737
 
0.1%
COLOMBIANA 378
 
0.1%
VENEZOLANA 285
 
< 0.1%
CUBANA 128
 
< 0.1%
COLOMBIA 68
 
< 0.1%
PERUANA 59
 
< 0.1%
ECUATORIANA / ESPAÑOLA 52
 
< 0.1%
VENEZUELA 48
 
< 0.1%
Other values (65) 208
 
< 0.1%
(Missing) 138459
 
22.8%

Length

2023-03-10T02:48:36.049221image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ecuatoriana 465969
99.3%
ecuador 1159
 
0.2%
107 737
 
0.2%
colombiana 380
 
0.1%
venezolana 290
 
0.1%
cubana 128
 
< 0.1%
86
 
< 0.1%
colombia 68
 
< 0.1%
peruana 60
 
< 0.1%
española 58
 
< 0.1%
Other values (60) 247
 
0.1%

Most occurring characters

ValueCountFrequency (%)
A 1401250
27.2%
O 468426
 
9.1%
E 468067
 
9.1%
C 467744
 
9.1%
U 467412
 
9.1%
N 467255
 
9.1%
R 467242
 
9.1%
I 466529
 
9.1%
T 466011
 
9.1%
D 1189
 
< 0.1%
Other values (30) 5674
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 5144153
99.9%
Decimal Number 2337
 
< 0.1%
Space Separator 177
 
< 0.1%
Other Punctuation 96
 
< 0.1%
Math Symbol 18
 
< 0.1%
Lowercase Letter 18
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 1401250
27.2%
O 468426
 
9.1%
E 468067
 
9.1%
C 467744
 
9.1%
U 467412
 
9.1%
N 467255
 
9.1%
R 467242
 
9.1%
I 466529
 
9.1%
T 466011
 
9.1%
D 1189
 
< 0.1%
Other values (15) 3028
 
0.1%
Decimal Number
ValueCountFrequency (%)
0 773
33.1%
1 769
32.9%
7 737
31.5%
2 31
 
1.3%
3 12
 
0.5%
4 8
 
0.3%
6 3
 
0.1%
9 2
 
0.1%
8 1
 
< 0.1%
5 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
/ 91
94.8%
. 5
 
5.2%
Space Separator
ValueCountFrequency (%)
177
100.0%
Math Symbol
ValueCountFrequency (%)
18
100.0%
Lowercase Letter
ValueCountFrequency (%)
ë 18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5144171
99.9%
Common 2628
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 1401250
27.2%
O 468426
 
9.1%
E 468067
 
9.1%
C 467744
 
9.1%
U 467412
 
9.1%
N 467255
 
9.1%
R 467242
 
9.1%
I 466529
 
9.1%
T 466011
 
9.1%
D 1189
 
< 0.1%
Other values (16) 3046
 
0.1%
Common
ValueCountFrequency (%)
0 773
29.4%
1 769
29.3%
7 737
28.0%
177
 
6.7%
/ 91
 
3.5%
2 31
 
1.2%
18
 
0.7%
3 12
 
0.5%
4 8
 
0.3%
. 5
 
0.2%
Other values (4) 7
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5146697
> 99.9%
None 84
 
< 0.1%
Math Operators 18
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 1401250
27.2%
O 468426
 
9.1%
E 468067
 
9.1%
C 467744
 
9.1%
U 467412
 
9.1%
N 467255
 
9.1%
R 467242
 
9.1%
I 466529
 
9.1%
T 466011
 
9.1%
D 1189
 
< 0.1%
Other values (27) 5572
 
0.1%
None
ValueCountFrequency (%)
Ñ 66
78.6%
ë 18
 
21.4%
Math Operators
ValueCountFrequency (%)
18
100.0%

ETNIA
Categorical

IMBALANCE  MISSING 

Distinct19
Distinct (%)< 0.1%
Missing149542
Missing (%)24.6%
Memory size4.6 MiB
Mestizo/a
250665 
Mestizo
132528 
Indígena
 
16603
Montubio/a
 
16105
Montubio
 
8631
Other values (14)
33390 

Length

Max length36
Median length9
Mean length8.7068322
Min length4

Characters and Unicode

Total characters3987050
Distinct characters28
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMestizo/a
2nd rowMestizo/a
3rd rowMestizo/a
4th rowMestizo/a
5th rowMestizo/a

Common Values

ValueCountFrequency (%)
Mestizo/a 250665
41.3%
Mestizo 132528
21.8%
Indígena 16603
 
2.7%
Montubio/a 16105
 
2.7%
Montubio 8631
 
1.4%
Indígena 6297
 
1.0%
Blanco/a 5547
 
0.9%
Afroecuatoriano/a o Afrodescendiente 5514
 
0.9%
Afrodescendiente 3885
 
0.6%
Blanco 3073
 
0.5%
Other values (9) 9074
 
1.5%
(Missing) 149542
24.6%

Length

2023-03-10T02:48:36.097776image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mestizo/a 250665
53.5%
mestizo 132528
28.3%
indígena 16603
 
3.5%
montubio/a 16105
 
3.4%
afrodescendiente 9399
 
2.0%
montubio 8631
 
1.8%
indígena 6297
 
1.3%
blanco/a 5547
 
1.2%
afroecuatoriano/a 5544
 
1.2%
o 5514
 
1.2%
Other values (9) 12117
 
2.6%

Most occurring characters

ValueCountFrequency (%)
o 482092
12.1%
e 452149
11.3%
t 429000
10.8%
i 423090
10.6%
M 412932
10.4%
s 392592
9.8%
z 383193
9.6%
a 330380
8.3%
/ 282987
7.1%
n 103716
 
2.6%
Other values (18) 294919
7.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3217005
80.7%
Uppercase Letter 463436
 
11.6%
Other Punctuation 282987
 
7.1%
Math Symbol 12594
 
0.3%
Space Separator 11028
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 482092
15.0%
e 452149
14.1%
t 429000
13.3%
i 423090
13.2%
s 392592
12.2%
z 383193
11.9%
a 330380
10.3%
n 103716
 
3.2%
d 41698
 
1.3%
u 35283
 
1.1%
Other values (8) 143812
 
4.5%
Uppercase Letter
ValueCountFrequency (%)
M 412932
89.1%
I 22900
 
4.9%
A 14943
 
3.2%
B 8620
 
1.9%
N 2916
 
0.6%
O 1125
 
0.2%
Math Symbol
ValueCountFrequency (%)
6297
50.0%
6297
50.0%
Other Punctuation
ValueCountFrequency (%)
/ 282987
100.0%
Space Separator
ValueCountFrequency (%)
11028
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3680441
92.3%
Common 306609
 
7.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 482092
13.1%
e 452149
12.3%
t 429000
11.7%
i 423090
11.5%
M 412932
11.2%
s 392592
10.7%
z 383193
10.4%
a 330380
9.0%
n 103716
 
2.8%
d 41698
 
1.1%
Other values (14) 229599
6.2%
Common
ValueCountFrequency (%)
/ 282987
92.3%
11028
 
3.6%
6297
 
2.1%
6297
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3957853
99.3%
None 16603
 
0.4%
Math Operators 12594
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 482092
12.2%
e 452149
11.4%
t 429000
10.8%
i 423090
10.7%
M 412932
10.4%
s 392592
9.9%
z 383193
9.7%
a 330380
8.3%
/ 282987
7.2%
n 103716
 
2.6%
Other values (15) 265722
6.7%
None
ValueCountFrequency (%)
í 16603
100.0%
Math Operators
ValueCountFrequency (%)
6297
50.0%
6297
50.0%

COD_PARROQUIA_RESIDE
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1238
Distinct (%)0.3%
Missing165315
Missing (%)27.2%
Infinite0
Infinite (%)0.0%
Mean119234.93
Minimum10101
Maximum900751
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2023-03-10T02:48:36.157168image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum10101
5-th percentile20450
Q190107
median110902
Q3170115
95-th percentile220350
Maximum900751
Range890650
Interquartile range (IQR)80008

Descriptive statistics

Standard deviation54692.295
Coefficient of variation (CV)0.45869358
Kurtosis1.8040074
Mean119234.93
Median Absolute Deviation (MAD)40200
Skewness0.27872148
Sum5.2719603 × 1010
Variance2.9912471 × 109
MonotonicityNot monotonic
2023-03-10T02:48:36.214744image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90112 26598
 
4.4%
90114 15643
 
2.6%
90104 9969
 
1.6%
90115 7562
 
1.2%
170155 6917
 
1.1%
170108 6145
 
1.0%
170111 5226
 
0.9%
90701 4703
 
0.8%
130106 3913
 
0.6%
60101 3807
 
0.6%
Other values (1228) 351666
57.9%
(Missing) 165315
27.2%
ValueCountFrequency (%)
10101 785
0.1%
10102 283
 
< 0.1%
10103 591
0.1%
10104 185
 
< 0.1%
10105 994
0.2%
10106 73
 
< 0.1%
10107 371
 
0.1%
10108 473
0.1%
10109 633
0.1%
10110 220
 
< 0.1%
ValueCountFrequency (%)
900751 1
 
< 0.1%
900451 21
 
< 0.1%
900151 3
 
< 0.1%
240352 814
0.1%
240351 213
 
< 0.1%
240350 3
 
< 0.1%
240304 264
 
< 0.1%
240303 119
 
< 0.1%
240302 193
 
< 0.1%
240301 301
 
< 0.1%

PARROQUIA_RESIDE
Categorical

HIGH CARDINALITY  MISSING 

Distinct1221
Distinct (%)0.3%
Missing165315
Missing (%)27.2%
Memory size4.6 MiB
TARQUI
 
29415
XIMENA
 
15643
FEBRES CORDERO
 
9969
PASCUALES
 
7562
CALDERON (CARAPUNGO)
 
6917
Other values (1216)
372643 

Length

Max length97
Median length48
Mean length11.481607
Min length4

Characters and Unicode

Total characters5076581
Distinct characters56
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)< 0.1%

Sample

1st rowCHIMBACALLE
2nd rowROCAFUERTE
3rd rowMONAY
4th rowEL VECINO
5th rowTOTORACOCHA

Common Values

ValueCountFrequency (%)
TARQUI 29415
 
4.8%
XIMENA 15643
 
2.6%
FEBRES CORDERO 9969
 
1.6%
PASCUALES 7562
 
1.2%
CALDERON (CARAPUNGO) 6917
 
1.1%
CHILLOGALLO 6145
 
1.0%
GUAMANÍ 3993
 
0.7%
LIZARZABURU 3807
 
0.6%
LA LIBERTAD 3778
 
0.6%
SUCRE 3765
 
0.6%
Other values (1211) 351155
57.8%
(Missing) 165315
27.2%

Length

2023-03-10T02:48:36.277403image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
san 38232
 
4.9%
de 30381
 
3.9%
tarqui 29421
 
3.8%
la 25628
 
3.3%
el 20638
 
2.7%
ximena 15643
 
2.0%
cordero 10534
 
1.4%
febres 10431
 
1.3%
alfaro 9436
 
1.2%
santa 9166
 
1.2%
Other values (1362) 575132
74.2%

Most occurring characters

ValueCountFrequency (%)
A 720550
14.2%
O 398952
 
7.9%
E 398272
 
7.8%
339081
 
6.7%
L 330726
 
6.5%
R 319109
 
6.3%
N 306212
 
6.0%
I 288783
 
5.7%
C 255379
 
5.0%
S 222101
 
4.4%
Other values (46) 1497416
29.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 4605838
90.7%
Space Separator 339081
 
6.7%
Open Punctuation 44869
 
0.9%
Close Punctuation 44755
 
0.9%
Math Symbol 16732
 
0.3%
Lowercase Letter 12058
 
0.2%
Decimal Number 8092
 
0.2%
Other Punctuation 4665
 
0.1%
Dash Punctuation 491
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 720550
15.6%
O 398952
 
8.7%
E 398272
 
8.6%
L 330726
 
7.2%
R 319109
 
6.9%
N 306212
 
6.6%
I 288783
 
6.3%
C 255379
 
5.5%
S 222101
 
4.8%
U 220214
 
4.8%
Other values (23) 1145540
24.9%
Decimal Number
ValueCountFrequency (%)
1 2533
31.3%
2 1727
21.3%
5 1408
17.4%
8 1102
13.6%
4 786
 
9.7%
0 348
 
4.3%
7 137
 
1.7%
6 40
 
0.5%
9 11
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
ç 5390
44.7%
â 2546
21.1%
ì 2249
18.7%
ë 1873
 
15.5%
Other Punctuation
ValueCountFrequency (%)
. 4528
97.1%
, 115
 
2.5%
: 22
 
0.5%
Open Punctuation
ValueCountFrequency (%)
( 44755
99.7%
114
 
0.3%
Dash Punctuation
ValueCountFrequency (%)
297
60.5%
- 194
39.5%
Space Separator
ValueCountFrequency (%)
339081
100.0%
Close Punctuation
ValueCountFrequency (%)
) 44755
100.0%
Math Symbol
ValueCountFrequency (%)
16732
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4617896
91.0%
Common 458685
 
9.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 720550
15.6%
O 398952
 
8.6%
E 398272
 
8.6%
L 330726
 
7.2%
R 319109
 
6.9%
N 306212
 
6.6%
I 288783
 
6.3%
C 255379
 
5.5%
S 222101
 
4.8%
U 220214
 
4.8%
Other values (27) 1157598
25.1%
Common
ValueCountFrequency (%)
339081
73.9%
( 44755
 
9.8%
) 44755
 
9.8%
16732
 
3.6%
. 4528
 
1.0%
1 2533
 
0.6%
2 1727
 
0.4%
5 1408
 
0.3%
8 1102
 
0.2%
4 786
 
0.2%
Other values (9) 1278
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4988754
98.3%
None 70684
 
1.4%
Math Operators 16732
 
0.3%
Punctuation 411
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 720550
14.4%
O 398952
 
8.0%
E 398272
 
8.0%
339081
 
6.8%
L 330726
 
6.6%
R 319109
 
6.4%
N 306212
 
6.1%
I 288783
 
5.8%
C 255379
 
5.1%
S 222101
 
4.5%
Other values (32) 1409589
28.3%
None
ValueCountFrequency (%)
Í 17288
24.5%
Á 14169
20.0%
É 9840
13.9%
Ó 7403
10.5%
ç 5390
 
7.6%
Ñ 5024
 
7.1%
Å 4788
 
6.8%
â 2546
 
3.6%
ì 2249
 
3.2%
ë 1873
 
2.6%
Math Operators
ValueCountFrequency (%)
16732
100.0%
Punctuation
ValueCountFrequency (%)
297
72.3%
114
 
27.7%

COD_CANTON_RESIDE
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct224
Distinct (%)0.1%
Missing165315
Missing (%)27.2%
Infinite0
Infinite (%)0.0%
Mean1192.1062
Minimum101
Maximum9007
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2023-03-10T02:48:36.335898image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile204
Q1901
median1109
Q31701
95-th percentile2203
Maximum9007
Range8906
Interquartile range (IQR)800

Descriptive statistics

Standard deviation546.89735
Coefficient of variation (CV)0.45876561
Kurtosis1.8045692
Mean1192.1062
Median Absolute Deviation (MAD)402
Skewness0.27849485
Sum5.2708858 × 108
Variance299096.71
MonotonicityNot monotonic
2023-03-10T02:48:36.391184image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1701 75136
 
12.4%
901 68164
 
11.2%
101 14368
 
2.4%
1801 12721
 
2.1%
1301 11995
 
2.0%
601 11759
 
1.9%
2301 10575
 
1.7%
1308 9109
 
1.5%
1101 9034
 
1.5%
501 8205
 
1.4%
Other values (214) 211083
34.7%
(Missing) 165315
27.2%
ValueCountFrequency (%)
101 14368
2.4%
102 141
 
< 0.1%
103 589
 
0.1%
104 136
 
< 0.1%
105 485
 
0.1%
106 73
 
< 0.1%
107 57
 
< 0.1%
108 182
 
< 0.1%
109 324
 
0.1%
110 72
 
< 0.1%
ValueCountFrequency (%)
9007 1
 
< 0.1%
9004 21
 
< 0.1%
9001 3
 
< 0.1%
2403 1907
 
0.3%
2402 3333
 
0.5%
2401 4037
 
0.7%
2302 1108
 
0.2%
2301 10575
1.7%
2204 323
 
0.1%
2203 968
 
0.2%

CANTON_RESIDE
Categorical

HIGH CARDINALITY  MISSING 

Distinct229
Distinct (%)0.1%
Missing165315
Missing (%)27.2%
Memory size4.6 MiB
DISTRITO METROPOLITANO DE QUITO
75136 
GUAYAQUIL
68164 
CUENCA
 
14368
AMBATO
 
12721
PORTOVIEJO
 
11995
Other values (224)
259765 

Length

Max length32
Median length28
Mean length12.041764
Min length3

Characters and Unicode

Total characters5324254
Distinct characters33
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowDISTRITO METROPOLITANO DE QUITO
2nd rowGUAYAQUIL
3rd rowCUENCA
4th rowCUENCA
5th rowCUENCA

Common Values

ValueCountFrequency (%)
DISTRITO METROPOLITANO DE QUITO 75136
 
12.4%
GUAYAQUIL 68164
 
11.2%
CUENCA 14368
 
2.4%
AMBATO 12721
 
2.1%
PORTOVIEJO 11995
 
2.0%
RIOBAMBA 11759
 
1.9%
SANTO DOMINGO 10575
 
1.7%
MANTA 9109
 
1.5%
LOJA 9034
 
1.5%
LATACUNGA 8205
 
1.4%
Other values (219) 211083
34.7%
(Missing) 165315
27.2%

Length

2023-03-10T02:48:36.447606image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de 78888
 
10.7%
quito 75597
 
10.3%
distrito 75136
 
10.2%
metropolitano 75136
 
10.2%
guayaquil 68164
 
9.3%
cuenca 14368
 
2.0%
ambato 12721
 
1.7%
portoviejo 11995
 
1.6%
riobamba 11759
 
1.6%
santo 10575
 
1.4%
Other values (259) 299591
40.8%

Most occurring characters

ValueCountFrequency (%)
A 662010
12.4%
O 601084
11.3%
I 502860
 
9.4%
T 482875
 
9.1%
E 316756
 
5.9%
U 312343
 
5.9%
292246
 
5.5%
L 275987
 
5.2%
R 273245
 
5.1%
N 234668
 
4.4%
Other values (23) 1370180
25.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 5025258
94.4%
Space Separator 292246
 
5.5%
Math Symbol 2173
 
< 0.1%
Lowercase Letter 2173
 
< 0.1%
Decimal Number 1356
 
< 0.1%
Open Punctuation 524
 
< 0.1%
Close Punctuation 524
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 662010
13.2%
O 601084
12.0%
I 502860
10.0%
T 482875
9.6%
E 316756
 
6.3%
U 312343
 
6.2%
L 275987
 
5.5%
R 273245
 
5.4%
N 234668
 
4.7%
D 215362
 
4.3%
Other values (16) 1148068
22.8%
Decimal Number
ValueCountFrequency (%)
2 678
50.0%
4 678
50.0%
Space Separator
ValueCountFrequency (%)
292246
100.0%
Math Symbol
ValueCountFrequency (%)
2173
100.0%
Lowercase Letter
ValueCountFrequency (%)
ë 2173
100.0%
Open Punctuation
ValueCountFrequency (%)
( 524
100.0%
Close Punctuation
ValueCountFrequency (%)
) 524
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5027431
94.4%
Common 296823
 
5.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 662010
13.2%
O 601084
12.0%
I 502860
10.0%
T 482875
9.6%
E 316756
 
6.3%
U 312343
 
6.2%
L 275987
 
5.5%
R 273245
 
5.4%
N 234668
 
4.7%
D 215362
 
4.3%
Other values (17) 1150241
22.9%
Common
ValueCountFrequency (%)
292246
98.5%
2173
 
0.7%
2 678
 
0.2%
4 678
 
0.2%
( 524
 
0.2%
) 524
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5314488
99.8%
None 7593
 
0.1%
Math Operators 2173
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 662010
12.5%
O 601084
11.3%
I 502860
 
9.5%
T 482875
 
9.1%
E 316756
 
6.0%
U 312343
 
5.9%
292246
 
5.5%
L 275987
 
5.2%
R 273245
 
5.1%
N 234668
 
4.4%
Other values (20) 1360414
25.6%
None
ValueCountFrequency (%)
Ñ 5420
71.4%
ë 2173
28.6%
Math Operators
ValueCountFrequency (%)
2173
100.0%

COD_PROV_RESIDE
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct25
Distinct (%)< 0.1%
Missing165315
Missing (%)27.2%
Infinite0
Infinite (%)0.0%
Mean11.887735
Minimum1
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2023-03-10T02:48:36.499285image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q19
median11
Q317
95-th percentile22
Maximum90
Range89
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.4735831
Coefficient of variation (CV)0.46043954
Kurtosis1.7931608
Mean11.887735
Median Absolute Deviation (MAD)4
Skewness0.28376817
Sum5256150
Variance29.960111
MonotonicityNot monotonic
2023-03-10T02:48:36.543841image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
9 100944
16.6%
17 89817
14.8%
13 43643
 
7.2%
12 23963
 
3.9%
18 19182
 
3.2%
7 18502
 
3.0%
1 17113
 
2.8%
6 16451
 
2.7%
5 15437
 
2.5%
11 14538
 
2.4%
Other values (15) 82559
13.6%
(Missing) 165315
27.2%
ValueCountFrequency (%)
1 17113
 
2.8%
2 7270
 
1.2%
3 4690
 
0.8%
4 4828
 
0.8%
5 15437
 
2.5%
6 16451
 
2.7%
7 18502
 
3.0%
8 10007
 
1.6%
9 100944
16.6%
10 13429
 
2.2%
ValueCountFrequency (%)
90 25
 
< 0.1%
24 9277
 
1.5%
23 11683
 
1.9%
22 3086
 
0.5%
21 4371
 
0.7%
20 305
 
0.1%
19 3118
 
0.5%
18 19182
 
3.2%
17 89817
14.8%
16 3193
 
0.5%

PROVINCIA_RESIDE
Categorical

HIGH CORRELATION  MISSING 

Distinct26
Distinct (%)< 0.1%
Missing165315
Missing (%)27.2%
Memory size4.6 MiB
GUAYAS
100944 
PICHINCHA
89817 
MANABI
43643 
LOS RIOS
23963 
TUNGURAHUA
19182 
Other values (21)
164600 

Length

Max length30
Median length16
Mean length8.0709263
Min length4

Characters and Unicode

Total characters3568552
Distinct characters26
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPICHINCHA
2nd rowGUAYAS
3rd rowAZUAY
4th rowAZUAY
5th rowAZUAY

Common Values

ValueCountFrequency (%)
GUAYAS 100944
16.6%
PICHINCHA 89817
14.8%
MANABI 43643
 
7.2%
LOS RIOS 23963
 
3.9%
TUNGURAHUA 19182
 
3.2%
EL ORO 18502
 
3.0%
AZUAY 17113
 
2.8%
CHIMBORAZO 16451
 
2.7%
COTOPAXI 15437
 
2.5%
LOJA 14538
 
2.4%
Other values (16) 82559
13.6%
(Missing) 165315
27.2%

Length

2023-03-10T02:48:36.591434image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
guayas 100944
18.5%
pichincha 89817
16.4%
manabi 43643
 
8.0%
los 35646
 
6.5%
rios 23963
 
4.4%
tungurahua 19182
 
3.5%
el 18502
 
3.4%
oro 18502
 
3.4%
azuay 17113
 
3.1%
chimborazo 16451
 
3.0%
Other values (26) 163033
29.8%

Most occurring characters

ValueCountFrequency (%)
A 665451
18.6%
I 341658
 
9.6%
C 248158
 
7.0%
O 241463
 
6.8%
S 240163
 
6.7%
H 238014
 
6.7%
N 211098
 
5.9%
U 197799
 
5.5%
G 135424
 
3.8%
R 127531
 
3.6%
Other values (16) 921793
25.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 3460861
97.0%
Space Separator 104647
 
2.9%
Math Symbol 1522
 
< 0.1%
Lowercase Letter 1522
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 665451
19.2%
I 341658
9.9%
C 248158
 
7.2%
O 241463
 
7.0%
S 240163
 
6.9%
H 238014
 
6.9%
N 211098
 
6.1%
U 197799
 
5.7%
G 135424
 
3.9%
R 127531
 
3.7%
Other values (13) 814102
23.5%
Space Separator
ValueCountFrequency (%)
104647
100.0%
Math Symbol
ValueCountFrequency (%)
1522
100.0%
Lowercase Letter
ValueCountFrequency (%)
ë 1522
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3462383
97.0%
Common 106169
 
3.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 665451
19.2%
I 341658
9.9%
C 248158
 
7.2%
O 241463
 
7.0%
S 240163
 
6.9%
H 238014
 
6.9%
N 211098
 
6.1%
U 197799
 
5.7%
G 135424
 
3.9%
R 127531
 
3.7%
Other values (14) 815624
23.6%
Common
ValueCountFrequency (%)
104647
98.6%
1522
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3562340
99.8%
None 4690
 
0.1%
Math Operators 1522
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 665451
18.7%
I 341658
 
9.6%
C 248158
 
7.0%
O 241463
 
6.8%
S 240163
 
6.7%
H 238014
 
6.7%
N 211098
 
5.9%
U 197799
 
5.6%
G 135424
 
3.8%
R 127531
 
3.6%
Other values (13) 915581
25.7%
None
ValueCountFrequency (%)
Ñ 3168
67.5%
ë 1522
32.5%
Math Operators
ValueCountFrequency (%)
1522
100.0%

PRD_ID
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct12
Distinct (%)< 0.1%
Missing134696
Missing (%)22.2%
Infinite0
Infinite (%)0.0%
Mean332.68377
Minimum47
Maximum914
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2023-03-10T02:48:36.633141image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum47
5-th percentile47
Q147
median47
Q3676
95-th percentile676
Maximum914
Range867
Interquartile range (IQR)629

Descriptive statistics

Standard deviation312.63395
Coefficient of variation (CV)0.93973312
Kurtosis-1.9507238
Mean332.68377
Median Absolute Deviation (MAD)0
Skewness0.18899669
Sum1.5728224 × 108
Variance97739.988
MonotonicityNot monotonic
2023-03-10T02:48:36.668473image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
47 256362
42.2%
676 209369
34.5%
447 3110
 
0.5%
811 1651
 
0.3%
451 847
 
0.1%
48 607
 
0.1%
810 369
 
0.1%
450 253
 
< 0.1%
914 91
 
< 0.1%
452 68
 
< 0.1%
Other values (2) 41
 
< 0.1%
(Missing) 134696
22.2%
ValueCountFrequency (%)
47 256362
42.2%
48 607
 
0.1%
447 3110
 
0.5%
450 253
 
< 0.1%
451 847
 
0.1%
452 68
 
< 0.1%
676 209369
34.5%
801 15
 
< 0.1%
802 26
 
< 0.1%
810 369
 
0.1%
ValueCountFrequency (%)
914 91
 
< 0.1%
811 1651
 
0.3%
810 369
 
0.1%
802 26
 
< 0.1%
801 15
 
< 0.1%
676 209369
34.5%
452 68
 
< 0.1%
451 847
 
0.1%
450 253
 
< 0.1%
447 3110
 
0.5%

SEGMENTACION_PERSONA
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct13
Distinct (%)< 0.1%
Missing134696
Missing (%)22.2%
Memory size4.6 MiB
POBLACION GENERAL
256362 
POLITICA DE ACCION AFIRMATIVA
209369 
MERITO TERRITORIAL
 
3110
DUAL FOCALIZADO FUERZA TERRESTRE
 
1651
DUAL FOCALIZADO MIES
 
847
Other values (8)
 
1429

Length

Max length66
Median length17
Mean length22.382304
Min length3

Characters and Unicode

Total characters10581637
Distinct characters25
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPOLITICA DE ACCION AFIRMATIVA
2nd rowPOBLACION GENERAL
3rd rowPOBLACION GENERAL
4th rowPOLITICA DE ACCION AFIRMATIVA
5th rowPOLITICA DE ACCION AFIRMATIVA

Common Values

ValueCountFrequency (%)
POBLACION GENERAL 256362
42.2%
POLITICA DE ACCION AFIRMATIVA 209369
34.5%
MERITO TERRITORIAL 3110
 
0.5%
DUAL FOCALIZADO FUERZA TERRESTRE 1651
 
0.3%
DUAL FOCALIZADO MIES 847
 
0.1%
GAR 607
 
0.1%
DUAL FOCALIZADO POLICIA 253
 
< 0.1%
DUAL FOCALIZADO FUERZA AÉREA 230
 
< 0.1%
DUAL FOCALIZADO FUERZA AÉREA 139
 
< 0.1%
DUAL FOCALIZADO CUERPO DE BOMBEROS 91
 
< 0.1%
Other values (3) 109
 
< 0.1%
(Missing) 134696
22.2%

Length

2023-03-10T02:48:36.714278image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
poblacion 256362
18.7%
general 256362
18.7%
de 209490
15.3%
politica 209369
15.3%
accion 209369
15.3%
afirmativa 209369
15.3%
dual 3305
 
0.2%
focalizado 3305
 
0.2%
merito 3110
 
0.2%
territorial 3110
 
0.2%
Other values (21) 6248
 
0.5%

Most occurring characters

ValueCountFrequency (%)
A 1576588
14.9%
I 1317553
12.5%
O 944941
8.9%
896631
8.5%
C 888283
8.4%
E 737173
7.0%
L 732111
6.9%
N 722315
 
6.8%
R 486580
 
4.6%
P 466199
 
4.4%
Other values (15) 1813263
17.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 9684516
91.5%
Space Separator 896631
 
8.5%
Math Symbol 245
 
< 0.1%
Lowercase Letter 245
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 1576588
16.3%
I 1317553
13.6%
O 944941
9.8%
C 888283
9.2%
E 737173
7.6%
L 732111
7.6%
N 722315
7.5%
R 486580
 
5.0%
P 466199
 
4.8%
T 431494
 
4.5%
Other values (11) 1381279
14.3%
Lowercase Letter
ValueCountFrequency (%)
â 230
93.9%
ì 15
 
6.1%
Space Separator
ValueCountFrequency (%)
896631
100.0%
Math Symbol
ValueCountFrequency (%)
245
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9684761
91.5%
Common 896876
 
8.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 1576588
16.3%
I 1317553
13.6%
O 944941
9.8%
C 888283
9.2%
E 737173
7.6%
L 732111
7.6%
N 722315
7.5%
R 486580
 
5.0%
P 466199
 
4.8%
T 431494
 
4.5%
Other values (13) 1381524
14.3%
Common
ValueCountFrequency (%)
896631
> 99.9%
245
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10581008
> 99.9%
None 384
 
< 0.1%
Math Operators 245
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 1576588
14.9%
I 1317553
12.5%
O 944941
8.9%
896631
8.5%
C 888283
8.4%
E 737173
7.0%
L 732111
6.9%
N 722315
 
6.8%
R 486580
 
4.6%
P 466199
 
4.4%
Other values (11) 1812634
17.1%
Math Operators
ValueCountFrequency (%)
245
100.0%
None
ValueCountFrequency (%)
â 230
59.9%
É 139
36.2%
ì 15
 
3.9%

POS_ID
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct472759
Distinct (%)> 99.9%
Missing134696
Missing (%)22.2%
Infinite0
Infinite (%)0.0%
Mean23146826
Minimum16425448
Maximum29581621
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2023-03-10T02:48:36.769080image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum16425448
5-th percentile16746792
Q121423152
median23750766
Q326579638
95-th percentile28354920
Maximum29581621
Range13156173
Interquartile range (IQR)5156486

Descriptive statistics

Standard deviation3845274.4
Coefficient of variation (CV)0.16612535
Kurtosis-1.0411236
Mean23146826
Median Absolute Deviation (MAD)2728354.5
Skewness-0.36973094
Sum1.0943078 × 1013
Variance1.4786136 × 1013
MonotonicityNot monotonic
2023-03-10T02:48:36.830072image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16430690 2
 
< 0.1%
16859944 2
 
< 0.1%
16662058 2
 
< 0.1%
16719369 2
 
< 0.1%
17038675 2
 
< 0.1%
16709177 2
 
< 0.1%
16621873 2
 
< 0.1%
16462144 2
 
< 0.1%
16845995 2
 
< 0.1%
25789835 1
 
< 0.1%
Other values (472749) 472749
77.8%
(Missing) 134696
 
22.2%
ValueCountFrequency (%)
16425448 1
< 0.1%
16425453 1
< 0.1%
16425469 1
< 0.1%
16425490 1
< 0.1%
16425497 1
< 0.1%
16425524 1
< 0.1%
16425534 1
< 0.1%
16425544 1
< 0.1%
16425565 1
< 0.1%
16425580 1
< 0.1%
ValueCountFrequency (%)
29581621 1
< 0.1%
29581614 1
< 0.1%
29581599 1
< 0.1%
29581594 1
< 0.1%
29581554 1
< 0.1%
29581543 1
< 0.1%
29581537 1
< 0.1%
29581506 1
< 0.1%
29581497 1
< 0.1%
29581489 1
< 0.1%

POS_NOTA
Real number (ℝ)

Distinct512
Distinct (%)0.1%
Missing134696
Missing (%)22.2%
Infinite0
Infinite (%)0.0%
Mean780.17397
Minimum392
Maximum1000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2023-03-10T02:48:36.890568image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum392
5-th percentile664
Q1729
median778
Q3830
95-th percentile906
Maximum1000
Range608
Interquartile range (IQR)101

Descriptive statistics

Standard deviation72.967398
Coefficient of variation (CV)0.093527086
Kurtosis-0.27610439
Mean780.17397
Median Absolute Deviation (MAD)51
Skewness0.11184105
Sum3.6884129 × 108
Variance5324.2412
MonotonicityNot monotonic
2023-03-10T02:48:36.945901image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
780 2589
 
0.4%
766 2567
 
0.4%
770 2559
 
0.4%
778 2558
 
0.4%
774 2556
 
0.4%
758 2547
 
0.4%
747 2545
 
0.4%
786 2539
 
0.4%
753 2538
 
0.4%
789 2533
 
0.4%
Other values (502) 447237
73.6%
(Missing) 134696
 
22.2%
ValueCountFrequency (%)
392 1
< 0.1%
433 1
< 0.1%
437 1
< 0.1%
439 1
< 0.1%
448 1
< 0.1%
453 1
< 0.1%
464 2
< 0.1%
466 2
< 0.1%
469 1
< 0.1%
471 1
< 0.1%
ValueCountFrequency (%)
1000 42
< 0.1%
999 4
 
< 0.1%
998 10
 
< 0.1%
997 14
 
< 0.1%
996 13
 
< 0.1%
995 14
 
< 0.1%
994 18
< 0.1%
993 24
< 0.1%
992 17
< 0.1%
991 38
< 0.1%

POS_PRIORIDAD
Categorical

Distinct5
Distinct (%)< 0.1%
Missing134696
Missing (%)22.2%
Memory size4.6 MiB
1.0
274986 
2.0
109895 
3.0
37246 
4.0
28443 
5.0
 
22198

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1418304
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row5.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 274986
45.3%
2.0 109895
 
18.1%
3.0 37246
 
6.1%
4.0 28443
 
4.7%
5.0 22198
 
3.7%
(Missing) 134696
22.2%

Length

2023-03-10T02:48:36.998008image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-10T02:48:37.043108image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 274986
58.2%
2.0 109895
 
23.2%
3.0 37246
 
7.9%
4.0 28443
 
6.0%
5.0 22198
 
4.7%

Most occurring characters

ValueCountFrequency (%)
. 472768
33.3%
0 472768
33.3%
1 274986
19.4%
2 109895
 
7.7%
3 37246
 
2.6%
4 28443
 
2.0%
5 22198
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 945536
66.7%
Other Punctuation 472768
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 472768
50.0%
1 274986
29.1%
2 109895
 
11.6%
3 37246
 
3.9%
4 28443
 
3.0%
5 22198
 
2.3%
Other Punctuation
ValueCountFrequency (%)
. 472768
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1418304
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 472768
33.3%
0 472768
33.3%
1 274986
19.4%
2 109895
 
7.7%
3 37246
 
2.6%
4 28443
 
2.0%
5 22198
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1418304
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 472768
33.3%
0 472768
33.3%
1 274986
19.4%
2 109895
 
7.7%
3 37246
 
2.6%
4 28443
 
2.0%
5 22198
 
1.6%

SEGMETO_CARRERA
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct15
Distinct (%)< 0.1%
Missing134696
Missing (%)22.2%
Memory size4.6 MiB
OFERTA PÚBLICA
346724 
OFERTA P√öBLICA
104869 
POLITICA DE ACCION AFIRMATIVA
 
17111
DUAL FOCALIZADO FUERZA TERRESTRE
 
1651
DUAL FOCALIZADO MIES
 
847
Other values (10)
 
1566

Length

Max length66
Median length14
Mean length14.871478
Min length3

Characters and Unicode

Total characters7030759
Distinct characters31
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPOLITICA DE ACCION AFIRMATIVA
2nd rowOFERTA PÚBLICA
3rd rowOFERTA PÚBLICA
4th rowOFERTA PÚBLICA
5th rowOFERTA PÚBLICA

Common Values

ValueCountFrequency (%)
OFERTA PÚBLICA 346724
57.1%
OFERTA P√öBLICA 104869
 
17.3%
POLITICA DE ACCION AFIRMATIVA 17111
 
2.8%
DUAL FOCALIZADO FUERZA TERRESTRE 1651
 
0.3%
DUAL FOCALIZADO MIES 847
 
0.1%
POBLACION GENERAL 685
 
0.1%
DUAL FOCALIZADO POLICIA 253
 
< 0.1%
DUAL FOCALIZADO FUERZA AÉREA 230
 
< 0.1%
DUAL FOCALIZADO FUERZA AÉREA 139
 
< 0.1%
DUAL FOCALIZADO CUERPO DE BOMBEROS 91
 
< 0.1%
Other values (5) 168
 
< 0.1%
(Missing) 134696
 
22.2%

Length

2023-03-10T02:48:37.089818image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
oferta 451593
45.8%
pública 346724
35.2%
p√öblica 104869
 
10.6%
de 17232
 
1.7%
politica 17111
 
1.7%
accion 17111
 
1.7%
afirmativa 17111
 
1.7%
focalizado 3305
 
0.3%
dual 3305
 
0.3%
fuerza 2020
 
0.2%
Other values (27) 5266
 
0.5%

Most occurring characters

ValueCountFrequency (%)
A 1003472
14.3%
I 542849
 
7.7%
512879
 
7.3%
C 507479
 
7.2%
O 494552
 
7.0%
T 489295
 
7.0%
E 479258
 
6.8%
R 477250
 
6.8%
L 476982
 
6.8%
F 474029
 
6.7%
Other values (21) 1572714
22.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 6307436
89.7%
Space Separator 512879
 
7.3%
Math Symbol 105114
 
1.5%
Lowercase Letter 105114
 
1.5%
Decimal Number 216
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 1003472
15.9%
I 542849
8.6%
C 507479
8.0%
O 494552
7.8%
T 489295
7.8%
E 479258
7.6%
R 477250
7.6%
L 476982
7.6%
F 474029
7.5%
P 469911
7.5%
Other values (12) 892359
14.1%
Decimal Number
ValueCountFrequency (%)
2 54
25.0%
0 54
25.0%
1 54
25.0%
9 54
25.0%
Lowercase Letter
ValueCountFrequency (%)
ö 104869
99.8%
â 230
 
0.2%
ì 15
 
< 0.1%
Space Separator
ValueCountFrequency (%)
512879
100.0%
Math Symbol
ValueCountFrequency (%)
105114
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6412550
91.2%
Common 618209
 
8.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 1003472
15.6%
I 542849
8.5%
C 507479
7.9%
O 494552
7.7%
T 489295
7.6%
E 479258
7.5%
R 477250
7.4%
L 476982
7.4%
F 474029
7.4%
P 469911
7.3%
Other values (15) 997473
15.6%
Common
ValueCountFrequency (%)
512879
83.0%
105114
 
17.0%
2 54
 
< 0.1%
0 54
 
< 0.1%
1 54
 
< 0.1%
9 54
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6473668
92.1%
None 451977
 
6.4%
Math Operators 105114
 
1.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 1003472
15.5%
I 542849
8.4%
512879
7.9%
C 507479
7.8%
O 494552
7.6%
T 489295
7.6%
E 479258
7.4%
R 477250
7.4%
L 476982
7.4%
F 474029
7.3%
Other values (15) 1015623
15.7%
None
ValueCountFrequency (%)
Ú 346724
76.7%
ö 104869
 
23.2%
â 230
 
0.1%
É 139
 
< 0.1%
ì 15
 
< 0.1%
Math Operators
ValueCountFrequency (%)
105114
100.0%

IES_ID
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct248
Distinct (%)0.1%
Missing134696
Missing (%)22.2%
Infinite0
Infinite (%)0.0%
Mean178.10219
Minimum22
Maximum1054
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2023-03-10T02:48:37.144383image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile22
Q151
median60
Q389
95-th percentile875
Maximum1054
Range1032
Interquartile range (IQR)38

Descriptive statistics

Standard deviation261.01568
Coefficient of variation (CV)1.4655388
Kurtosis2.968754
Mean178.10219
Median Absolute Deviation (MAD)25
Skewness2.0514168
Sum84201018
Variance68129.186
MonotonicityNot monotonic
2023-03-10T02:48:37.203587image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59 64172
 
10.6%
51 38426
 
6.3%
22 33316
 
5.5%
86 25514
 
4.2%
46 24748
 
4.1%
31 18400
 
3.0%
102 15324
 
2.5%
89 11843
 
1.9%
48 11416
 
1.9%
83 10856
 
1.8%
Other values (238) 218753
36.0%
(Missing) 134696
22.2%
ValueCountFrequency (%)
22 33316
5.5%
23 2720
 
0.4%
29 6678
 
1.1%
30 4829
 
0.8%
31 18400
3.0%
32 5052
 
0.8%
38 1312
 
0.2%
39 4118
 
0.7%
43 21
 
< 0.1%
44 573
 
0.1%
ValueCountFrequency (%)
1054 54
 
< 0.1%
1053 35
 
< 0.1%
1051 304
 
0.1%
1050 69
 
< 0.1%
1049 6
 
< 0.1%
1047 35
 
< 0.1%
1046 1041
0.2%
1045 102
 
< 0.1%
1040 1111
0.2%
1034 1390
0.2%

IES_SIGLAS_INSTIT
Categorical

HIGH CARDINALITY  HIGH CORRELATION  MISSING 

Distinct67
Distinct (%)< 0.1%
Missing217088
Missing (%)35.7%
Memory size4.6 MiB
UNEMI
64172 
UG
38426 
ESPE
33316 
UTM
25514 
UCE
24748 
Other values (62)
204200 

Length

Max length12
Median length11
Mean length4.1097967
Min length2

Characters and Unicode

Total characters1604366
Distinct characters54
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUTM
2nd rowUNEMI
3rd rowUCUENCA
4th rowUNEMI
5th rowUNL

Common Values

ValueCountFrequency (%)
UNEMI 64172
 
10.6%
UG 38426
 
6.3%
ESPE 33316
 
5.5%
UTM 25514
 
4.2%
UCE 24748
 
4.1%
ESPOCH 18400
 
3.0%
ULEAM 15324
 
2.5%
UTEQ 11843
 
1.9%
UCUENCA 11416
 
1.9%
UTB 10856
 
1.8%
Other values (57) 136361
22.4%
(Missing) 217088
35.7%

Length

2023-03-10T02:48:37.409224image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
unemi 64172
16.4%
ug 38426
 
9.8%
espe 33316
 
8.5%
utm 25514
 
6.5%
uce 24748
 
6.3%
espoch 18400
 
4.7%
uleam 15324
 
3.9%
uteq 11843
 
3.0%
ucuenca 11416
 
2.9%
utb 10856
 
2.8%
Other values (57) 136361
34.9%

Most occurring characters

ValueCountFrequency (%)
U 328057
20.4%
E 284337
17.7%
M 128089
 
8.0%
N 121366
 
7.6%
T 107847
 
6.7%
C 97919
 
6.1%
S 85747
 
5.3%
P 82330
 
5.1%
A 76157
 
4.7%
I 75001
 
4.7%
Other values (44) 217516
13.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1567086
97.7%
Lowercase Letter 25981
 
1.6%
Dash Punctuation 6511
 
0.4%
Decimal Number 4640
 
0.3%
Connector Punctuation 148
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U 328057
20.9%
E 284337
18.1%
M 128089
 
8.2%
N 121366
 
7.7%
T 107847
 
6.9%
C 97919
 
6.2%
S 85747
 
5.5%
P 82330
 
5.3%
A 76157
 
4.9%
I 75001
 
4.8%
Other values (12) 180236
11.5%
Lowercase Letter
ValueCountFrequency (%)
u 4346
16.7%
c 3935
15.1%
i 3154
12.1%
s 2163
8.3%
e 1899
7.3%
g 1623
 
6.2%
a 1409
 
5.4%
o 1382
 
5.3%
f 1222
 
4.7%
p 1029
 
4.0%
Other values (11) 3819
14.7%
Decimal Number
ValueCountFrequency (%)
2 1881
40.5%
1 1037
22.3%
8 809
17.4%
0 482
 
10.4%
5 129
 
2.8%
7 103
 
2.2%
3 103
 
2.2%
4 58
 
1.2%
6 38
 
0.8%
Dash Punctuation
ValueCountFrequency (%)
- 6511
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 148
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1593067
99.3%
Common 11299
 
0.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
U 328057
20.6%
E 284337
17.8%
M 128089
 
8.0%
N 121366
 
7.6%
T 107847
 
6.8%
C 97919
 
6.1%
S 85747
 
5.4%
P 82330
 
5.2%
A 76157
 
4.8%
I 75001
 
4.7%
Other values (33) 206217
12.9%
Common
ValueCountFrequency (%)
- 6511
57.6%
2 1881
 
16.6%
1 1037
 
9.2%
8 809
 
7.2%
0 482
 
4.3%
_ 148
 
1.3%
5 129
 
1.1%
7 103
 
0.9%
3 103
 
0.9%
4 58
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1604366
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U 328057
20.4%
E 284337
17.7%
M 128089
 
8.0%
N 121366
 
7.6%
T 107847
 
6.7%
C 97919
 
6.1%
S 85747
 
5.3%
P 82330
 
5.1%
A 76157
 
4.7%
I 75001
 
4.7%
Other values (44) 217516
13.6%

NOMBRE_INSTITUCION
Categorical

HIGH CARDINALITY  MISSING 

Distinct399
Distinct (%)0.1%
Missing134696
Missing (%)22.2%
Memory size4.6 MiB
UNIVERSIDAD ESTATAL DE MILAGRO
64172 
UNIVERSIDAD DE GUAYAQUIL
38426 
UNIVERSIDAD DE LAS FUERZAS ARMADAS (ESPE)
33316 
UNIVERSIDAD TECNICA DE MANABI
 
25514
UNIVERSIDAD CENTRAL DEL ECUADOR
 
24748
Other values (394)
286592 

Length

Max length84
Median length76
Mean length35.130705
Min length15

Characters and Unicode

Total characters16608673
Distinct characters54
Distinct categories12 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowINSTITUTO TECNOLÓGICO SUPERIOR LIBERTAD
2nd rowUNIVERSIDAD TECNICA DE MANABI
3rd rowUNIVERSIDAD ESTATAL DE MILAGRO
4th rowUNIVERSIDAD DE CUENCA
5th rowINSTITUTO TECNOLÓGICO SUPERIOR LUIS ROGERIO GONZALEZ

Common Values

ValueCountFrequency (%)
UNIVERSIDAD ESTATAL DE MILAGRO 64172
 
10.6%
UNIVERSIDAD DE GUAYAQUIL 38426
 
6.3%
UNIVERSIDAD DE LAS FUERZAS ARMADAS (ESPE) 33316
 
5.5%
UNIVERSIDAD TECNICA DE MANABI 25514
 
4.2%
UNIVERSIDAD CENTRAL DEL ECUADOR 24748
 
4.1%
ESCUELA SUPERIOR POLITECNICA DE CHIMBORAZO 18400
 
3.0%
UNIVERSIDAD LAICA ELOY ALFARO DE MANABI 15324
 
2.5%
UNIVERSIDAD TECNICA ESTATAL DE QUEVEDO 11843
 
1.9%
UNIVERSIDAD DE CUENCA 11416
 
1.9%
UNIVERSIDAD TECNICA DE BABAHOYO 10856
 
1.8%
Other values (389) 218753
36.0%
(Missing) 134696
22.2%

Length

2023-03-10T02:48:37.470132image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
universidad 350993
16.5%
de 330828
15.5%
superior 115091
 
5.4%
estatal 110218
 
5.2%
tecnica 87819
 
4.1%
instituto 86535
 
4.1%
milagro 64172
 
3.0%
tecnológico 63791
 
3.0%
del 61899
 
2.9%
manabi 54947
 
2.6%
Other values (414) 802702
37.7%

Most occurring characters

ValueCountFrequency (%)
A 1756372
10.6%
1656571
 
10.0%
I 1655072
 
10.0%
E 1635298
 
9.8%
D 1198388
 
7.2%
R 974188
 
5.9%
S 957195
 
5.8%
N 938874
 
5.7%
U 855429
 
5.2%
O 837599
 
5.0%
Other values (44) 4143687
24.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 14815762
89.2%
Space Separator 1656571
 
10.0%
Close Punctuation 34521
 
0.2%
Open Punctuation 34521
 
0.2%
Math Symbol 27718
 
0.2%
Lowercase Letter 27419
 
0.2%
Decimal Number 4340
 
< 0.1%
Modifier Symbol 3529
 
< 0.1%
Dash Punctuation 2095
 
< 0.1%
Other Punctuation 830
 
< 0.1%
Other values (2) 1367
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 1756372
11.9%
I 1655072
11.2%
E 1635298
11.0%
D 1198388
 
8.1%
R 974188
 
6.6%
S 957195
 
6.5%
N 938874
 
6.3%
U 855429
 
5.8%
O 837599
 
5.7%
T 835530
 
5.6%
Other values (23) 3171817
21.4%
Lowercase Letter
ValueCountFrequency (%)
ì 21629
78.9%
ç 2146
 
7.8%
ü 1463
 
5.3%
â 1374
 
5.0%
ë 520
 
1.9%
ö 287
 
1.0%
Decimal Number
ValueCountFrequency (%)
1 2134
49.2%
7 2134
49.2%
0 54
 
1.2%
2 18
 
0.4%
Math Symbol
ValueCountFrequency (%)
26899
97.0%
¬ 819
 
3.0%
Other Punctuation
ValueCountFrequency (%)
. 787
94.8%
' 43
 
5.2%
Space Separator
ValueCountFrequency (%)
1656571
100.0%
Close Punctuation
ValueCountFrequency (%)
) 34521
100.0%
Open Punctuation
ValueCountFrequency (%)
( 34521
100.0%
Modifier Symbol
ValueCountFrequency (%)
´ 3529
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2095
100.0%
Currency Symbol
ValueCountFrequency (%)
¥ 819
100.0%
Other Letter
ValueCountFrequency (%)
º 548
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 14843729
89.4%
Common 1764944
 
10.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 1756372
11.8%
I 1655072
11.1%
E 1635298
11.0%
D 1198388
 
8.1%
R 974188
 
6.6%
S 957195
 
6.4%
N 938874
 
6.3%
U 855429
 
5.8%
O 837599
 
5.6%
T 835530
 
5.6%
Other values (30) 3199784
21.6%
Common
ValueCountFrequency (%)
1656571
93.9%
) 34521
 
2.0%
( 34521
 
2.0%
26899
 
1.5%
´ 3529
 
0.2%
1 2134
 
0.1%
7 2134
 
0.1%
- 2095
 
0.1%
¥ 819
 
< 0.1%
¬ 819
 
< 0.1%
Other values (4) 902
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16459061
99.1%
None 122713
 
0.7%
Math Operators 26899
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 1756372
10.7%
1656571
10.1%
I 1655072
10.1%
E 1635298
9.9%
D 1198388
 
7.3%
R 974188
 
5.9%
S 957195
 
5.8%
N 938874
 
5.7%
U 855429
 
5.2%
O 837599
 
5.1%
Other values (26) 3994075
24.3%
None
ValueCountFrequency (%)
Ó 73157
59.6%
ì 21629
 
17.6%
Í 9460
 
7.7%
É 3825
 
3.1%
´ 3529
 
2.9%
ç 2146
 
1.7%
ü 1463
 
1.2%
â 1374
 
1.1%
Ñ 1139
 
0.9%
Á 1033
 
0.8%
Other values (7) 3958
 
3.2%
Math Operators
ValueCountFrequency (%)
26899
100.0%

TIPO_INSTITUCION
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing134696
Missing (%)22.2%
Memory size4.6 MiB
U
385952 
I
86816 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters472768
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowI
2nd rowU
3rd rowU
4th rowU
5th rowI

Common Values

ValueCountFrequency (%)
U 385952
63.5%
I 86816
 
14.3%
(Missing) 134696
 
22.2%

Length

2023-03-10T02:48:37.521095image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-10T02:48:37.562484image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
u 385952
81.6%
i 86816
 
18.4%

Most occurring characters

ValueCountFrequency (%)
U 385952
81.6%
I 86816
 
18.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 472768
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U 385952
81.6%
I 86816
 
18.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 472768
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
U 385952
81.6%
I 86816
 
18.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 472768
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U 385952
81.6%
I 86816
 
18.4%

TIPO_FINANCIAMIENTO
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing134696
Missing (%)22.2%
Memory size4.6 MiB
PÚBLICA
340388 
P√öBLICA
106873 
AUTOFINANCIADA
 
14913
COFINANCIADA
 
10594

Length

Max length14
Median length7
Mean length7.5589084
Min length7

Characters and Unicode

Total characters3573610
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAUTOFINANCIADA
2nd rowPÚBLICA
3rd rowPÚBLICA
4th rowPÚBLICA
5th rowPÚBLICA

Common Values

ValueCountFrequency (%)
PÚBLICA 340388
56.0%
P√öBLICA 106873
 
17.6%
AUTOFINANCIADA 14913
 
2.5%
COFINANCIADA 10594
 
1.7%
(Missing) 134696
 
22.2%

Length

2023-03-10T02:48:37.599766image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-10T02:48:37.648043image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
pública 340388
72.0%
p√öblica 106873
 
22.6%
autofinanciada 14913
 
3.2%
cofinanciada 10594
 
2.2%

Most occurring characters

ValueCountFrequency (%)
A 538695
15.1%
I 498275
13.9%
C 483362
13.5%
P 447261
12.5%
B 447261
12.5%
L 447261
12.5%
Ú 340388
9.5%
106873
 
3.0%
ö 106873
 
3.0%
N 51014
 
1.4%
Other values (5) 106347
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 3359864
94.0%
Math Symbol 106873
 
3.0%
Lowercase Letter 106873
 
3.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 538695
16.0%
I 498275
14.8%
C 483362
14.4%
P 447261
13.3%
B 447261
13.3%
L 447261
13.3%
Ú 340388
10.1%
N 51014
 
1.5%
O 25507
 
0.8%
F 25507
 
0.8%
Other values (3) 55333
 
1.6%
Math Symbol
ValueCountFrequency (%)
106873
100.0%
Lowercase Letter
ValueCountFrequency (%)
ö 106873
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3466737
97.0%
Common 106873
 
3.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 538695
15.5%
I 498275
14.4%
C 483362
13.9%
P 447261
12.9%
B 447261
12.9%
L 447261
12.9%
Ú 340388
9.8%
ö 106873
 
3.1%
N 51014
 
1.5%
O 25507
 
0.7%
Other values (4) 80840
 
2.3%
Common
ValueCountFrequency (%)
106873
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3019476
84.5%
None 447261
 
12.5%
Math Operators 106873
 
3.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 538695
17.8%
I 498275
16.5%
C 483362
16.0%
P 447261
14.8%
B 447261
14.8%
L 447261
14.8%
N 51014
 
1.7%
O 25507
 
0.8%
F 25507
 
0.8%
D 25507
 
0.8%
Other values (2) 29826
 
1.0%
None
ValueCountFrequency (%)
Ú 340388
76.1%
ö 106873
 
23.9%
Math Operators
ValueCountFrequency (%)
106873
100.0%

CAMPUS_NOMBRE
Categorical

HIGH CARDINALITY  MISSING 

Distinct184
Distinct (%)< 0.1%
Missing134696
Missing (%)22.2%
Memory size4.6 MiB
MATRIZ - MILAGRO
64023 
MATRIZ - GUAYAQUIL
55400 
MATRIZ - QUITO
37514 
MATRIZ - PORTOVIEJO
 
24299
MATRIZ - CAMPUS CENTRAL
 
24070
Other values (179)
267462 

Length

Max length72
Median length42
Mean length16.199493
Min length4

Characters and Unicode

Total characters7658602
Distinct characters45
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowMATRIZ - Sede QUITO
2nd rowMATRIZ - PORTOVIEJO
3rd rowMATRIZ - MILAGRO
4th rowMATRIZ - AZUAY.
5th rowMATRIZ - AZOGUES

Common Values

ValueCountFrequency (%)
MATRIZ - MILAGRO 64023
 
10.5%
MATRIZ - GUAYAQUIL 55400
 
9.1%
MATRIZ - QUITO 37514
 
6.2%
MATRIZ - PORTOVIEJO 24299
 
4.0%
MATRIZ - CAMPUS CENTRAL 24070
 
4.0%
MATRIZ - RIOBAMBA 23669
 
3.9%
MATRIZ - AMBATO 12426
 
2.0%
MATRIZ - MANTA 12331
 
2.0%
MATRIZ - QUEVEDO 11843
 
1.9%
MATRIZ - AZUAY. 11416
 
1.9%
Other values (174) 195777
32.2%
(Missing) 134696
22.2%

Length

2023-03-10T02:48:37.699645image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
402525
29.1%
matriz 395728
28.6%
milagro 65557
 
4.7%
guayaquil 61967
 
4.5%
quito 45078
 
3.3%
riobamba 27460
 
2.0%
portoviejo 24513
 
1.8%
campus 24070
 
1.7%
central 24070
 
1.7%
latacunga 15735
 
1.1%
Other values (126) 297728
21.5%

Most occurring characters

ValueCountFrequency (%)
A 1080961
14.1%
912954
11.9%
I 705414
9.2%
R 608002
 
7.9%
T 588011
 
7.7%
M 576647
 
7.5%
Z 418133
 
5.5%
- 405551
 
5.3%
O 371372
 
4.8%
U 281779
 
3.7%
Other values (35) 1709778
22.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 6320588
82.5%
Space Separator 912954
 
11.9%
Dash Punctuation 405551
 
5.3%
Other Punctuation 13507
 
0.2%
Lowercase Letter 3039
 
< 0.1%
Open Punctuation 1125
 
< 0.1%
Close Punctuation 1125
 
< 0.1%
Math Symbol 713
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 1080961
17.1%
I 705414
11.2%
R 608002
9.6%
T 588011
9.3%
M 576647
9.1%
Z 418133
 
6.6%
O 371372
 
5.9%
U 281779
 
4.5%
L 273840
 
4.3%
E 218955
 
3.5%
Other values (17) 1197474
18.9%
Lowercase Letter
ValueCountFrequency (%)
e 1464
48.2%
d 762
25.1%
ë 369
 
12.1%
ç 183
 
6.0%
ì 161
 
5.3%
a 30
 
1.0%
i 20
 
0.7%
c 20
 
0.7%
n 10
 
0.3%
é 10
 
0.3%
Other Punctuation
ValueCountFrequency (%)
. 12382
91.7%
, 1125
 
8.3%
Space Separator
ValueCountFrequency (%)
912954
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 405551
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1125
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1125
100.0%
Math Symbol
ValueCountFrequency (%)
713
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6323627
82.6%
Common 1334975
 
17.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 1080961
17.1%
I 705414
11.2%
R 608002
9.6%
T 588011
9.3%
M 576647
9.1%
Z 418133
 
6.6%
O 371372
 
5.9%
U 281779
 
4.5%
L 273840
 
4.3%
E 218955
 
3.5%
Other values (28) 1200513
19.0%
Common
ValueCountFrequency (%)
912954
68.4%
- 405551
30.4%
. 12382
 
0.9%
( 1125
 
0.1%
, 1125
 
0.1%
) 1125
 
0.1%
713
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7654875
> 99.9%
None 3014
 
< 0.1%
Math Operators 713
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 1080961
14.1%
912954
11.9%
I 705414
9.2%
R 608002
 
7.9%
T 588011
 
7.7%
M 576647
 
7.5%
Z 418133
 
5.5%
- 405551
 
5.3%
O 371372
 
4.9%
U 281779
 
3.7%
Other values (27) 1706051
22.3%
None
ValueCountFrequency (%)
Ñ 1133
37.6%
Í 613
20.3%
Ó 545
18.1%
ë 369
 
12.2%
ç 183
 
6.1%
ì 161
 
5.3%
é 10
 
0.3%
Math Operators
ValueCountFrequency (%)
713
100.0%

CAM_ID
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct388
Distinct (%)0.1%
Missing134696
Missing (%)22.2%
Infinite0
Infinite (%)0.0%
Mean479.65464
Minimum4
Maximum1632
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2023-03-10T02:48:37.756585image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile29
Q1171
median294
Q3623
95-th percentile1317
Maximum1632
Range1628
Interquartile range (IQR)452

Descriptive statistics

Standard deviation465.42849
Coefficient of variation (CV)0.97034084
Kurtosis-0.31851663
Mean479.65464
Median Absolute Deviation (MAD)262
Skewness1.0152623
Sum2.2676537 × 108
Variance216623.68
MonotonicityNot monotonic
2023-03-10T02:48:37.811672image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32 64023
 
10.5%
29 38347
 
6.3%
1317 24748
 
4.1%
171 23222
 
3.8%
294 22874
 
3.8%
1311 15286
 
2.5%
299 11843
 
1.9%
743 11416
 
1.9%
308 10785
 
1.8%
297 9563
 
1.6%
Other values (378) 240661
39.6%
(Missing) 134696
22.2%
ValueCountFrequency (%)
4 65
 
< 0.1%
11 77
 
< 0.1%
29 38347
6.3%
32 64023
10.5%
33 9549
 
1.6%
35 1307
 
0.2%
117 796
 
0.1%
119 649
 
0.1%
120 30
 
< 0.1%
122 39
 
< 0.1%
ValueCountFrequency (%)
1632 90
 
< 0.1%
1630 106
 
< 0.1%
1628 15
 
< 0.1%
1627 2
 
< 0.1%
1626 44
 
< 0.1%
1624 6
 
< 0.1%
1619 88
 
< 0.1%
1618 263
 
< 0.1%
1615 873
0.1%
1612 60
 
< 0.1%

CAMPUS_CIUDAD
Categorical

HIGH CARDINALITY  HIGH CORRELATION  MISSING 

Distinct100
Distinct (%)< 0.1%
Missing134696
Missing (%)22.2%
Memory size4.6 MiB
MILAGRO
65557 
GUAYAQUIL
62713 
QUITO
53145 
RIOBAMBA
26256 
PORTOVIEJO
24513 
Other values (95)
240584 

Length

Max length31
Median length21
Mean length7.8077429
Min length4

Characters and Unicode

Total characters3691251
Distinct characters30
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowQUITO
2nd rowPORTOVIEJO
3rd rowMILAGRO
4th rowCUENCA
5th rowAZOGUES

Common Values

ValueCountFrequency (%)
MILAGRO 65557
10.8%
GUAYAQUIL 62713
10.3%
QUITO 53145
 
8.7%
RIOBAMBA 26256
 
4.3%
PORTOVIEJO 24513
 
4.0%
SANGOLQUI 23274
 
3.8%
LATACUNGA 15735
 
2.6%
CUENCA 15225
 
2.5%
QUEVEDO 15118
 
2.5%
AMBATO 15017
 
2.5%
Other values (90) 156215
25.7%
(Missing) 134696
22.2%

Length

2023-03-10T02:48:37.868748image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
milagro 65557
 
12.4%
guayaquil 62713
 
11.8%
quito 53422
 
10.1%
riobamba 27460
 
5.2%
portoviejo 24513
 
4.6%
sangolqui 23274
 
4.4%
latacunga 15735
 
3.0%
cuenca 15225
 
2.9%
quevedo 15118
 
2.9%
ambato 15017
 
2.8%
Other values (100) 212312
40.0%

Most occurring characters

ValueCountFrequency (%)
A 629276
17.0%
O 375109
 
10.2%
I 332376
 
9.0%
U 284408
 
7.7%
L 256322
 
6.9%
G 185950
 
5.0%
R 179125
 
4.9%
T 166705
 
4.5%
M 164400
 
4.5%
Q 161050
 
4.4%
Other values (20) 956530
25.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 3629753
98.3%
Space Separator 59632
 
1.6%
Other Punctuation 966
 
< 0.1%
Math Symbol 450
 
< 0.1%
Lowercase Letter 450
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 629276
17.3%
O 375109
10.3%
I 332376
 
9.2%
U 284408
 
7.8%
L 256322
 
7.1%
G 185950
 
5.1%
R 179125
 
4.9%
T 166705
 
4.6%
M 164400
 
4.5%
Q 161050
 
4.4%
Other values (15) 895032
24.7%
Lowercase Letter
ValueCountFrequency (%)
ë 267
59.3%
ç 183
40.7%
Space Separator
ValueCountFrequency (%)
59632
100.0%
Other Punctuation
ValueCountFrequency (%)
. 966
100.0%
Math Symbol
ValueCountFrequency (%)
450
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3630203
98.3%
Common 61048
 
1.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 629276
17.3%
O 375109
10.3%
I 332376
 
9.2%
U 284408
 
7.8%
L 256322
 
7.1%
G 185950
 
5.1%
R 179125
 
4.9%
T 166705
 
4.6%
M 164400
 
4.5%
Q 161050
 
4.4%
Other values (17) 895482
24.7%
Common
ValueCountFrequency (%)
59632
97.7%
. 966
 
1.6%
450
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3688743
99.9%
None 2058
 
0.1%
Math Operators 450
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 629276
17.1%
O 375109
 
10.2%
I 332376
 
9.0%
U 284408
 
7.7%
L 256322
 
6.9%
G 185950
 
5.0%
R 179125
 
4.9%
T 166705
 
4.5%
M 164400
 
4.5%
Q 161050
 
4.4%
Other values (15) 954022
25.9%
None
ValueCountFrequency (%)
Ñ 995
48.3%
Í 613
29.8%
ë 267
 
13.0%
ç 183
 
8.9%
Math Operators
ValueCountFrequency (%)
450
100.0%

PROVINCIA
Categorical

HIGH CORRELATION  MISSING 

Distinct25
Distinct (%)< 0.1%
Missing134696
Missing (%)22.2%
Memory size4.6 MiB
GUAYAS
132275 
PICHINCHA
79645 
MANABI
58316 
LOS RIOS
28009 
CHIMBORAZO
28003 
Other values (20)
146520 

Length

Max length30
Median length16
Mean length7.7356166
Min length4

Characters and Unicode

Total characters3657152
Distinct characters26
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowPICHINCHA
2nd rowMANABI
3rd rowGUAYAS
4th rowAZUAY
5th rowCAÑAR

Common Values

ValueCountFrequency (%)
GUAYAS 132275
21.8%
PICHINCHA 79645
13.1%
MANABI 58316
9.6%
LOS RIOS 28009
 
4.6%
CHIMBORAZO 28003
 
4.6%
COTOPAXI 19214
 
3.2%
TUNGURAHUA 16649
 
2.7%
IMBABURA 15469
 
2.5%
AZUAY 15265
 
2.5%
LOJA 12359
 
2.0%
Other values (15) 67564
11.1%
(Missing) 134696
22.2%

Length

2023-03-10T02:48:37.916473image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
guayas 132275
23.8%
pichincha 79645
14.3%
manabi 58316
10.5%
los 36446
 
6.6%
rios 28009
 
5.0%
chimborazo 28003
 
5.0%
cotopaxi 19214
 
3.5%
tungurahua 16649
 
3.0%
imbabura 15469
 
2.8%
azuay 15265
 
2.7%
Other values (23) 126170
22.7%

Most occurring characters

ValueCountFrequency (%)
A 732785
20.0%
I 345375
 
9.4%
S 256140
 
7.0%
O 239574
 
6.6%
C 233747
 
6.4%
H 219562
 
6.0%
U 216920
 
5.9%
N 198069
 
5.4%
G 159403
 
4.4%
Y 147540
 
4.0%
Other values (16) 908037
24.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 3571549
97.7%
Space Separator 82693
 
2.3%
Math Symbol 1455
 
< 0.1%
Lowercase Letter 1455
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 732785
20.5%
I 345375
9.7%
S 256140
 
7.2%
O 239574
 
6.7%
C 233747
 
6.5%
H 219562
 
6.1%
U 216920
 
6.1%
N 198069
 
5.5%
G 159403
 
4.5%
Y 147540
 
4.1%
Other values (13) 822434
23.0%
Space Separator
ValueCountFrequency (%)
82693
100.0%
Math Symbol
ValueCountFrequency (%)
1455
100.0%
Lowercase Letter
ValueCountFrequency (%)
ë 1455
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3573004
97.7%
Common 84148
 
2.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 732785
20.5%
I 345375
9.7%
S 256140
 
7.2%
O 239574
 
6.7%
C 233747
 
6.5%
H 219562
 
6.1%
U 216920
 
6.1%
N 198069
 
5.5%
G 159403
 
4.5%
Y 147540
 
4.1%
Other values (14) 823889
23.1%
Common
ValueCountFrequency (%)
82693
98.3%
1455
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3650590
99.8%
None 5107
 
0.1%
Math Operators 1455
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 732785
20.1%
I 345375
 
9.5%
S 256140
 
7.0%
O 239574
 
6.6%
C 233747
 
6.4%
H 219562
 
6.0%
U 216920
 
5.9%
N 198069
 
5.4%
G 159403
 
4.4%
Y 147540
 
4.0%
Other values (13) 901475
24.7%
None
ValueCountFrequency (%)
Ñ 3652
71.5%
ë 1455
 
28.5%
Math Operators
ValueCountFrequency (%)
1455
100.0%

CANTON
Categorical

HIGH CARDINALITY  HIGH CORRELATION  MISSING 

Distinct83
Distinct (%)< 0.1%
Missing134696
Missing (%)22.2%
Memory size4.6 MiB
MILAGRO
65557 
GUAYAQUIL
62713 
DISTRITO METROPOLITANO DE QUITO
53413 
RIOBAMBA
27460 
PORTOVIEJO
24513 
Other values (78)
239112 

Length

Max length31
Median length20
Mean length10.545707
Min length4

Characters and Unicode

Total characters4985673
Distinct characters27
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowDISTRITO METROPOLITANO DE QUITO
2nd rowPORTOVIEJO
3rd rowMILAGRO
4th rowCUENCA
5th rowAZOGUES

Common Values

ValueCountFrequency (%)
MILAGRO 65557
10.8%
GUAYAQUIL 62713
 
10.3%
DISTRITO METROPOLITANO DE QUITO 53413
 
8.8%
RIOBAMBA 27460
 
4.5%
PORTOVIEJO 24513
 
4.0%
LATACUNGA 15735
 
2.6%
CUENCA 15225
 
2.5%
QUEVEDO 15118
 
2.5%
AMBATO 15017
 
2.5%
RUMIÑAHUI 14887
 
2.5%
Other values (73) 163130
26.9%
(Missing) 134696
22.2%

Length

2023-03-10T02:48:37.964869image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
milagro 65557
 
9.8%
guayaquil 62713
 
9.4%
de 57564
 
8.6%
quito 53422
 
8.0%
distrito 53413
 
8.0%
metropolitano 53413
 
8.0%
riobamba 27460
 
4.1%
portoviejo 24513
 
3.7%
latacunga 15735
 
2.3%
cuenca 15225
 
2.3%
Other values (87) 240881
36.0%

Most occurring characters

ValueCountFrequency (%)
A 687450
13.8%
O 551873
11.1%
I 507785
 
10.2%
T 367736
 
7.4%
U 313966
 
6.3%
R 311540
 
6.2%
L 272184
 
5.5%
E 236945
 
4.8%
M 233789
 
4.7%
197128
 
4.0%
Other values (17) 1305277
26.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 4770515
95.7%
Space Separator 197128
 
4.0%
Math Symbol 9015
 
0.2%
Lowercase Letter 9015
 
0.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 687450
14.4%
O 551873
11.6%
I 507785
10.6%
T 367736
 
7.7%
U 313966
 
6.6%
R 311540
 
6.5%
L 272184
 
5.7%
E 236945
 
5.0%
M 233789
 
4.9%
G 172446
 
3.6%
Other values (14) 1114801
23.4%
Space Separator
ValueCountFrequency (%)
197128
100.0%
Math Symbol
ValueCountFrequency (%)
9015
100.0%
Lowercase Letter
ValueCountFrequency (%)
ë 9015
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4779530
95.9%
Common 206143
 
4.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 687450
14.4%
O 551873
11.5%
I 507785
10.6%
T 367736
 
7.7%
U 313966
 
6.6%
R 311540
 
6.5%
L 272184
 
5.7%
E 236945
 
5.0%
M 233789
 
4.9%
G 172446
 
3.6%
Other values (15) 1123816
23.5%
Common
ValueCountFrequency (%)
197128
95.6%
9015
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4952014
99.3%
None 24644
 
0.5%
Math Operators 9015
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 687450
13.9%
O 551873
11.1%
I 507785
 
10.3%
T 367736
 
7.4%
U 313966
 
6.3%
R 311540
 
6.3%
L 272184
 
5.5%
E 236945
 
4.8%
M 233789
 
4.7%
197128
 
4.0%
Other values (14) 1271618
25.7%
None
ValueCountFrequency (%)
Ñ 15629
63.4%
ë 9015
36.6%
Math Operators
ValueCountFrequency (%)
9015
100.0%

PARROQUIA
Categorical

HIGH CARDINALITY  MISSING 

Distinct127
Distinct (%)< 0.1%
Missing134696
Missing (%)22.2%
Memory size4.6 MiB
MILAGRO, CABECERA CANTONAL
65557 
GUAYAQUIL, CABECERA CANTONAL Y CAPITAL PROVINCIAL
62689 
QUITO DISTRITO METROPOLITANO, CABECERA CANTONAL, CAPITAL PROVINCIAL Y DE LA REPUBLICA DEL ECUADOR
40481 
RIOBAMBA, CABECERA CANTONAL Y CAPITAL PROVINCIAL
27445 
PORTOVIEJO
 
24513
Other values (122)
252083 

Length

Max length97
Median length48
Mean length34.376049
Min length4

Characters and Unicode

Total characters16251896
Distinct characters42
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowQUITO DISTRITO METROPOLITANO, CABECERA CANTONAL, CAPITAL PROVINCIAL Y DE LA REPUBLICA DEL ECUADOR
2nd rowPORTOVIEJO
3rd rowMILAGRO, CABECERA CANTONAL
4th rowCUENCA, CABECERA CANTONAL Y CAPITAL PROVINCIAL.
5th rowAZOGUES

Common Values

ValueCountFrequency (%)
MILAGRO, CABECERA CANTONAL 65557
 
10.8%
GUAYAQUIL, CABECERA CANTONAL Y CAPITAL PROVINCIAL 62689
 
10.3%
QUITO DISTRITO METROPOLITANO, CABECERA CANTONAL, CAPITAL PROVINCIAL Y DE LA REPUBLICA DEL ECUADOR 40481
 
6.7%
RIOBAMBA, CABECERA CANTONAL Y CAPITAL PROVINCIAL 27445
 
4.5%
PORTOVIEJO 24513
 
4.0%
LATACUNGA, CABECERA CANTONAL Y CAPITAL PROVINCIAL 15735
 
2.6%
SANGOLQUÍ 14583
 
2.4%
CUENCA, CABECERA CANTONAL Y CAPITAL PROVINCIAL. 14439
 
2.4%
QUEVEDO 13993
 
2.3%
BABAHOYO, CABECERA CANTONAL Y CAPITAL PROVINCIAL 12616
 
2.1%
Other values (117) 180717
29.7%
(Missing) 134696
22.2%

Length

2023-03-10T02:48:38.020531image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cantonal 301690
14.4%
cabecera 301690
14.4%
y 209492
 
10.0%
capital 209492
 
10.0%
provincial 209492
 
10.0%
milagro 65557
 
3.1%
de 63291
 
3.0%
guayaquil 62689
 
3.0%
la 61093
 
2.9%
quito 40490
 
1.9%
Other values (149) 570432
27.2%

Most occurring characters

ValueCountFrequency (%)
A 2657154
16.3%
1622640
10.0%
C 1525058
9.4%
L 1170134
 
7.2%
I 1106093
 
6.8%
O 1095213
 
6.7%
E 996192
 
6.1%
N 987732
 
6.1%
R 868407
 
5.3%
T 817927
 
5.0%
Other values (32) 3405346
21.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 14242022
87.6%
Space Separator 1622640
 
10.0%
Other Punctuation 356610
 
2.2%
Math Symbol 13261
 
0.1%
Lowercase Letter 11646
 
0.1%
Open Punctuation 2861
 
< 0.1%
Close Punctuation 2843
 
< 0.1%
Dash Punctuation 13
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 2657154
18.7%
C 1525058
10.7%
L 1170134
8.2%
I 1106093
7.8%
O 1095213
7.7%
E 996192
 
7.0%
N 987732
 
6.9%
R 868407
 
6.1%
T 817927
 
5.7%
P 553973
 
3.9%
Other values (20) 2464139
17.3%
Lowercase Letter
ValueCountFrequency (%)
ç 9193
78.9%
ë 2277
 
19.6%
ì 148
 
1.3%
â 28
 
0.2%
Other Punctuation
ValueCountFrequency (%)
, 342171
96.0%
. 14439
 
4.0%
Open Punctuation
ValueCountFrequency (%)
( 2843
99.4%
18
 
0.6%
Space Separator
ValueCountFrequency (%)
1622640
100.0%
Math Symbol
ValueCountFrequency (%)
13261
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2843
100.0%
Dash Punctuation
ValueCountFrequency (%)
13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 14253668
87.7%
Common 1998228
 
12.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 2657154
18.6%
C 1525058
10.7%
L 1170134
8.2%
I 1106093
7.8%
O 1095213
7.7%
E 996192
 
7.0%
N 987732
 
6.9%
R 868407
 
6.1%
T 817927
 
5.7%
P 553973
 
3.9%
Other values (24) 2475785
17.4%
Common
ValueCountFrequency (%)
1622640
81.2%
, 342171
 
17.1%
. 14439
 
0.7%
13261
 
0.7%
) 2843
 
0.1%
( 2843
 
0.1%
18
 
< 0.1%
13
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16195154
99.7%
None 43450
 
0.3%
Math Operators 13261
 
0.1%
Punctuation 31
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 2657154
16.4%
1622640
10.0%
C 1525058
9.4%
L 1170134
 
7.2%
I 1106093
 
6.8%
O 1095213
 
6.8%
E 996192
 
6.2%
N 987732
 
6.1%
R 868407
 
5.4%
T 817927
 
5.1%
Other values (18) 3348604
20.7%
None
ValueCountFrequency (%)
Í 16348
37.6%
ç 9193
21.2%
Ñ 8277
19.0%
Á 5160
 
11.9%
ë 2277
 
5.2%
Å 1633
 
3.8%
Ó 278
 
0.6%
ì 148
 
0.3%
É 90
 
0.2%
â 28
 
0.1%
Math Operators
ValueCountFrequency (%)
13261
100.0%
Punctuation
ValueCountFrequency (%)
18
58.1%
13
41.9%

CCP_ID
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct9076
Distinct (%)1.9%
Missing134696
Missing (%)22.2%
Infinite0
Infinite (%)0.0%
Mean26243.815
Minimum19105
Maximum32288
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2023-03-10T02:48:38.077272image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum19105
5-th percentile19894
Q124456
median26617
Q329462
95-th percentile31667
Maximum32288
Range13183
Interquartile range (IQR)5006

Descriptive statistics

Standard deviation3803.3819
Coefficient of variation (CV)0.14492489
Kurtosis-1.0312584
Mean26243.815
Median Absolute Deviation (MAD)2706
Skewness-0.39491345
Sum1.2407236 × 1010
Variance14465714
MonotonicityNot monotonic
2023-03-10T02:48:38.136532image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30222 2156
 
0.4%
26006 1988
 
0.3%
30214 1917
 
0.3%
30219 1899
 
0.3%
30194 1898
 
0.3%
30198 1787
 
0.3%
30201 1648
 
0.3%
30203 1595
 
0.3%
30216 1590
 
0.3%
20401 1537
 
0.3%
Other values (9066) 454753
74.9%
(Missing) 134696
 
22.2%
ValueCountFrequency (%)
19105 24
< 0.1%
19109 57
< 0.1%
19113 22
 
< 0.1%
19117 21
 
< 0.1%
19121 23
< 0.1%
19125 23
< 0.1%
19129 20
 
< 0.1%
19133 4
 
< 0.1%
19134 2
 
< 0.1%
19135 1
 
< 0.1%
ValueCountFrequency (%)
32288 2
 
< 0.1%
32268 50
< 0.1%
32256 70
< 0.1%
32255 69
< 0.1%
32254 40
< 0.1%
32253 30
 
< 0.1%
32252 55
< 0.1%
32251 60
< 0.1%
32250 57
< 0.1%
32249 77
< 0.1%

CUS_ID_PADRE
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct20080
Distinct (%)4.2%
Missing134696
Missing (%)22.2%
Infinite0
Infinite (%)0.0%
Mean290394.75
Minimum267051
Maximum313609
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2023-03-10T02:48:38.190605image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum267051
5-th percentile267582
Q1285857
median294112
Q3303874
95-th percentile309017
Maximum313609
Range46558
Interquartile range (IQR)18017

Descriptive statistics

Standard deviation13677.546
Coefficient of variation (CV)0.047099838
Kurtosis-0.98312307
Mean290394.75
Median Absolute Deviation (MAD)9610
Skewness-0.42686121
Sum1.3728935 × 1011
Variance1.8707526 × 108
MonotonicityNot monotonic
2023-03-10T02:48:38.248305image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
304816 1618
 
0.3%
304599 1596
 
0.3%
304600 1572
 
0.3%
304825 1570
 
0.3%
286847 1410
 
0.2%
286435 1407
 
0.2%
286434 1404
 
0.2%
304814 1344
 
0.2%
267730 1126
 
0.2%
304597 1044
 
0.2%
Other values (20070) 458677
75.5%
(Missing) 134696
 
22.2%
ValueCountFrequency (%)
267051 204
< 0.1%
267052 81
 
< 0.1%
267053 81
 
< 0.1%
267054 166
< 0.1%
267055 37
 
< 0.1%
267056 44
 
< 0.1%
267057 114
< 0.1%
267058 31
 
< 0.1%
267059 22
 
< 0.1%
267060 8
 
< 0.1%
ValueCountFrequency (%)
313609 4
< 0.1%
313607 2
 
< 0.1%
313606 1
 
< 0.1%
313605 6
< 0.1%
313604 1
 
< 0.1%
313603 1
 
< 0.1%
313602 4
< 0.1%
313600 4
< 0.1%
313599 1
 
< 0.1%
313598 2
 
< 0.1%

CUS_ID_HIJO
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct30618
Distinct (%)6.5%
Missing134696
Missing (%)22.2%
Infinite0
Infinite (%)0.0%
Mean290774.47
Minimum267051
Maximum313609
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2023-03-10T02:48:38.303173image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum267051
5-th percentile267685
Q1285893
median294116
Q3303931
95-th percentile309298
Maximum313609
Range46558
Interquartile range (IQR)18038

Descriptive statistics

Standard deviation13681.429
Coefficient of variation (CV)0.047051687
Kurtosis-0.98122101
Mean290774.47
Median Absolute Deviation (MAD)9636
Skewness-0.42271823
Sum1.3746886 × 1011
Variance1.8718151 × 108
MonotonicityNot monotonic
2023-03-10T02:48:38.360590image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
304825 1500
 
0.2%
304600 1500
 
0.2%
304599 1500
 
0.2%
286434 1300
 
0.2%
304816 1191
 
0.2%
286847 1167
 
0.2%
286435 1108
 
0.2%
304597 1000
 
0.2%
267730 1000
 
0.2%
304827 900
 
0.1%
Other values (30608) 460602
75.8%
(Missing) 134696
 
22.2%
ValueCountFrequency (%)
267051 200
< 0.1%
267052 80
 
< 0.1%
267053 80
 
< 0.1%
267054 158
< 0.1%
267055 31
 
< 0.1%
267056 38
 
< 0.1%
267057 100
< 0.1%
267058 22
 
< 0.1%
267059 16
 
< 0.1%
267060 8
 
< 0.1%
ValueCountFrequency (%)
313609 4
< 0.1%
313607 2
 
< 0.1%
313606 1
 
< 0.1%
313605 6
< 0.1%
313604 1
 
< 0.1%
313603 1
 
< 0.1%
313602 4
< 0.1%
313600 4
< 0.1%
313599 1
 
< 0.1%
313598 2
 
< 0.1%

CAR_ID
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct417
Distinct (%)0.1%
Missing134696
Missing (%)22.2%
Infinite0
Infinite (%)0.0%
Mean5491.6851
Minimum4447
Maximum7603
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2023-03-10T02:48:38.414886image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum4447
5-th percentile4462
Q14748
median5205
Q35482
95-th percentile7257
Maximum7603
Range3156
Interquartile range (IQR)734

Descriptive statistics

Standard deviation982.88984
Coefficient of variation (CV)0.17897782
Kurtosis-0.65717128
Mean5491.6851
Median Absolute Deviation (MAD)448
Skewness0.90935167
Sum2.596293 × 109
Variance966072.44
MonotonicityNot monotonic
2023-03-10T02:48:38.469735image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5457 21097
 
3.5%
4473 18790
 
3.1%
4478 17137
 
2.8%
5334 16958
 
2.8%
4911 15733
 
2.6%
5455 14744
 
2.4%
5205 13734
 
2.3%
4458 13190
 
2.2%
4618 12947
 
2.1%
4781 11434
 
1.9%
Other values (407) 317004
52.2%
(Missing) 134696
22.2%
ValueCountFrequency (%)
4447 22
 
< 0.1%
4458 13190
2.2%
4459 2958
 
0.5%
4460 1262
 
0.2%
4461 3406
 
0.6%
4462 4963
 
0.8%
4470 45
 
< 0.1%
4473 18790
3.1%
4474 5739
 
0.9%
4478 17137
2.8%
ValueCountFrequency (%)
7603 63
< 0.1%
7602 37
< 0.1%
7601 42
< 0.1%
7600 55
< 0.1%
7597 5
 
< 0.1%
7596 3
 
< 0.1%
7595 1
 
< 0.1%
7593 91
< 0.1%
7587 6
 
< 0.1%
7586 2
 
< 0.1%

CARRERA
Categorical

HIGH CARDINALITY  MISSING 

Distinct740
Distinct (%)0.2%
Missing134696
Missing (%)22.2%
Memory size4.6 MiB
TURISMO
 
16282
EDUCACION INICIAL
 
13980
EDUCACION BASICA
 
12593
TECNOLOGIAS DE LA INFORMACION
 
12587
ECONOMIA
 
12290
Other values (735)
405036 

Length

Max length179
Median length89
Mean length25.224486
Min length4

Characters and Unicode

Total characters11925330
Distinct characters42
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)< 0.1%

Sample

1st rowTECNOLOGIA SUPERIOR EN PODOLOGIA
2nd rowTURISMO
3rd rowPSICOLOGIA
4th rowARTES VISUALES
5th rowTECNOLOGIA SUPERIOR EN ELECTRICIDAD

Common Values

ValueCountFrequency (%)
TURISMO 16282
 
2.7%
EDUCACION INICIAL 13980
 
2.3%
EDUCACION BASICA 12593
 
2.1%
TECNOLOGIAS DE LA INFORMACION 12587
 
2.1%
ECONOMIA 12290
 
2.0%
ADMINISTRACION DE EMPRESAS 11151
 
1.8%
COMUNICACION 11057
 
1.8%
CONTABILIDAD Y AUDITORIA 9697
 
1.6%
PEDAGOGIA DE LOS IDIOMAS NACIONALES Y EXTRANJEROS LICENCIADO/A EN PEDAGOGIA DEL IDIOMA INGLES 9551
 
1.6%
DERECHO 8484
 
1.4%
Other values (730) 355096
58.5%
(Missing) 134696
 
22.2%

Length

2023-03-10T02:48:38.535752image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
en 104564
 
7.1%
de 104084
 
7.1%
tecnologia 88501
 
6.0%
superior 88265
 
6.0%
y 66810
 
4.5%
pedagogia 53837
 
3.7%
educacion 41096
 
2.8%
la 40570
 
2.8%
ingenieria 31729
 
2.2%
administracion 23868
 
1.6%
Other values (437) 827568
56.3%

Most occurring characters

ValueCountFrequency (%)
I 1411219
11.8%
A 1269727
10.6%
E 1179504
9.9%
1113600
9.3%
O 1070082
9.0%
N 854702
 
7.2%
C 764750
 
6.4%
R 652556
 
5.5%
S 511199
 
4.3%
T 504317
 
4.2%
Other values (32) 2593674
21.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 10783803
90.4%
Space Separator 1113600
 
9.3%
Other Punctuation 21317
 
0.2%
Math Symbol 2968
 
< 0.1%
Lowercase Letter 2968
 
< 0.1%
Dash Punctuation 674
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 1411219
13.1%
A 1269727
11.8%
E 1179504
10.9%
O 1070082
9.9%
N 854702
7.9%
C 764750
 
7.1%
R 652556
 
6.1%
S 511199
 
4.7%
T 504317
 
4.7%
L 489749
 
4.5%
Other values (23) 2075998
19.3%
Lowercase Letter
ValueCountFrequency (%)
ë 2483
83.7%
ì 176
 
5.9%
ç 158
 
5.3%
ú 151
 
5.1%
Other Punctuation
ValueCountFrequency (%)
/ 20735
97.3%
, 582
 
2.7%
Space Separator
ValueCountFrequency (%)
1113600
100.0%
Math Symbol
ValueCountFrequency (%)
2968
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 674
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10786771
90.5%
Common 1138559
 
9.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 1411219
13.1%
A 1269727
11.8%
E 1179504
10.9%
O 1070082
9.9%
N 854702
7.9%
C 764750
 
7.1%
R 652556
 
6.0%
S 511199
 
4.7%
T 504317
 
4.7%
L 489749
 
4.5%
Other values (27) 2078966
19.3%
Common
ValueCountFrequency (%)
1113600
97.8%
/ 20735
 
1.8%
2968
 
0.3%
- 674
 
0.1%
, 582
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11910780
99.9%
None 11582
 
0.1%
Math Operators 2968
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 1411219
11.8%
A 1269727
10.7%
E 1179504
9.9%
1113600
9.3%
O 1070082
9.0%
N 854702
 
7.2%
C 764750
 
6.4%
R 652556
 
5.5%
S 511199
 
4.3%
T 504317
 
4.2%
Other values (20) 2579124
21.7%
None
ValueCountFrequency (%)
Ñ 7532
65.0%
ë 2483
 
21.4%
Í 341
 
2.9%
Ü 306
 
2.6%
Ó 287
 
2.5%
ì 176
 
1.5%
ç 158
 
1.4%
ú 151
 
1.3%
É 102
 
0.9%
Á 27
 
0.2%
Math Operators
ValueCountFrequency (%)
2968
100.0%

AREA
Categorical

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)< 0.1%
Missing134696
Missing (%)22.2%
Memory size4.6 MiB
INGENIERIA, INDUSTRIA Y CONSTRUCCION
90621 
EDUCACION
76695 
ADMINISTRACION
66485 
CIENCIAS SOCIALES, PERIODISMO, INFORMACION Y DERECHO
65825 
TECNOLOGIAS DE LA INFORMACION Y LA COMUNICACION (TIC)
40274 
Other values (12)
132868 

Length

Max length53
Median length48
Mean length29.193433
Min length8

Characters and Unicode

Total characters13801721
Distinct characters30
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSALUD Y BIENESTAR
2nd rowSERVICIOS
3rd rowCIENCIAS SOCIALES, PERIODISMO, INFORMACION Y DERECHO
4th rowARTES Y HUMANIDADES
5th rowINGENIERIA, INDUSTRIA Y CONSTRUCCION

Common Values

ValueCountFrequency (%)
INGENIERIA, INDUSTRIA Y CONSTRUCCION 90621
14.9%
EDUCACION 76695
12.6%
ADMINISTRACION 66485
10.9%
CIENCIAS SOCIALES, PERIODISMO, INFORMACION Y DERECHO 65825
10.8%
TECNOLOGIAS DE LA INFORMACION Y LA COMUNICACION (TIC) 40274
 
6.6%
SERVICIOS 37732
 
6.2%
SALUD Y BIENESTAR 32107
 
5.3%
AGRICULTURA, SILVICULTURA, PESCA Y VETERINARIA 30830
 
5.1%
ARTES Y HUMANIDADES 14386
 
2.4%
CIENCIAS NATURALES, MATEMATICAS Y ESTADISTICA 11003
 
1.8%
Other values (7) 6810
 
1.1%
(Missing) 134696
22.2%

Length

2023-03-10T02:48:38.591473image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
y 291735
17.8%
informacion 106099
 
6.5%
ingenieria 90621
 
5.5%
construccion 90621
 
5.5%
industria 90621
 
5.5%
ciencias 82691
 
5.0%
la 80548
 
4.9%
educacion 77153
 
4.7%
sociales 67246
 
4.1%
administracion 66485
 
4.0%
Other values (25) 598572
36.4%

Most occurring characters

ValueCountFrequency (%)
I 1880075
13.6%
C 1263709
9.2%
A 1211064
8.8%
N 1172650
8.5%
1169624
8.5%
O 1002584
 
7.3%
E 960846
 
7.0%
S 869124
 
6.3%
R 832700
 
6.0%
T 548716
 
4.0%
Other values (20) 2890629
20.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 12247408
88.7%
Space Separator 1169624
 
8.5%
Other Punctuation 300715
 
2.2%
Open Punctuation 40274
 
0.3%
Close Punctuation 40274
 
0.3%
Math Symbol 2284
 
< 0.1%
Lowercase Letter 1142
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 1880075
15.4%
C 1263709
10.3%
A 1211064
9.9%
N 1172650
9.6%
O 1002584
8.2%
E 960846
7.8%
S 869124
7.1%
R 832700
6.8%
T 548716
 
4.5%
U 485716
 
4.0%
Other values (14) 2020224
16.5%
Space Separator
ValueCountFrequency (%)
1169624
100.0%
Other Punctuation
ValueCountFrequency (%)
, 300715
100.0%
Open Punctuation
ValueCountFrequency (%)
( 40274
100.0%
Close Punctuation
ValueCountFrequency (%)
) 40274
100.0%
Math Symbol
ValueCountFrequency (%)
2284
100.0%
Lowercase Letter
ValueCountFrequency (%)
ç 1142
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 12248550
88.7%
Common 1553171
 
11.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 1880075
15.3%
C 1263709
10.3%
A 1211064
9.9%
N 1172650
9.6%
O 1002584
8.2%
E 960846
7.8%
S 869124
7.1%
R 832700
6.8%
T 548716
 
4.5%
U 485716
 
4.0%
Other values (15) 2021366
16.5%
Common
ValueCountFrequency (%)
1169624
75.3%
, 300715
 
19.4%
( 40274
 
2.6%
) 40274
 
2.6%
2284
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13788830
99.9%
None 10607
 
0.1%
Math Operators 2284
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 1880075
13.6%
C 1263709
9.2%
A 1211064
8.8%
N 1172650
8.5%
1169624
8.5%
O 1002584
 
7.3%
E 960846
 
7.0%
S 869124
 
6.3%
R 832700
 
6.0%
T 548716
 
4.0%
Other values (14) 2877738
20.9%
None
ValueCountFrequency (%)
Á 4142
39.0%
Í 4142
39.0%
Å 1142
 
10.8%
ç 1142
 
10.8%
Ó 39
 
0.4%
Math Operators
ValueCountFrequency (%)
2284
100.0%

SUBAREA
Categorical

HIGH CORRELATION  MISSING 

Distinct30
Distinct (%)< 0.1%
Missing134696
Missing (%)22.2%
Memory size4.6 MiB
EDUCACION
76695 
EDUCACION COMERCIAL Y ADMINISTRACION
66809 
INGENIERIA Y PROFESIONES AFINES
56241 
TECNOLOGIAS DE LA INFORMACION Y LA COMUNICACION (TIC)
40274 
CIENCIAS SOCIALES Y DEL COMPORTAMIENTO
38922 
Other values (25)
193827 

Length

Max length53
Median length31
Mean length24.235274
Min length5

Characters and Unicode

Total characters11457662
Distinct characters25
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSALUD
2nd rowSERVICIOS PERSONALES
3rd rowCIENCIAS SOCIALES Y DEL COMPORTAMIENTO
4th rowARTES
5th rowINGENIERIA Y PROFESIONES AFINES

Common Values

ValueCountFrequency (%)
EDUCACION 76695
12.6%
EDUCACION COMERCIAL Y ADMINISTRACION 66809
11.0%
INGENIERIA Y PROFESIONES AFINES 56241
9.3%
TECNOLOGIAS DE LA INFORMACION Y LA COMUNICACION (TIC) 40274
 
6.6%
CIENCIAS SOCIALES Y DEL COMPORTAMIENTO 38922
 
6.4%
SERVICIOS PERSONALES 29924
 
4.9%
SALUD 28336
 
4.7%
INDUSTRIA Y PRODUCCION 22571
 
3.7%
AGRICULTURA 21195
 
3.5%
PERIODISMO E INFORMACION 15445
 
2.5%
Other values (20) 76356
12.6%
(Missing) 134696
22.2%

Length

2023-03-10T02:48:38.639251image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
y 247089
16.9%
educacion 143504
 
9.8%
la 80548
 
5.5%
comercial 66809
 
4.6%
administracion 66809
 
4.6%
afines 63282
 
4.3%
ingenieria 56241
 
3.9%
profesiones 56241
 
3.9%
informacion 55719
 
3.8%
ciencias 49925
 
3.4%
Other values (37) 572474
39.2%

Most occurring characters

ValueCountFrequency (%)
I 1430649
12.5%
C 1111639
9.7%
A 1025691
9.0%
O 1020361
8.9%
985873
8.6%
E 973515
8.5%
N 946770
8.3%
S 677821
 
5.9%
R 601700
 
5.3%
D 409249
 
3.6%
Other values (15) 2274394
19.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 10391241
90.7%
Space Separator 985873
 
8.6%
Open Punctuation 40274
 
0.4%
Close Punctuation 40274
 
0.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 1430649
13.8%
C 1111639
10.7%
A 1025691
9.9%
O 1020361
9.8%
E 973515
9.4%
N 946770
9.1%
S 677821
 
6.5%
R 601700
 
5.8%
D 409249
 
3.9%
L 356495
 
3.4%
Other values (12) 1837351
17.7%
Space Separator
ValueCountFrequency (%)
985873
100.0%
Open Punctuation
ValueCountFrequency (%)
( 40274
100.0%
Close Punctuation
ValueCountFrequency (%)
) 40274
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10391241
90.7%
Common 1066421
 
9.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 1430649
13.8%
C 1111639
10.7%
A 1025691
9.9%
O 1020361
9.8%
E 973515
9.4%
N 946770
9.1%
S 677821
 
6.5%
R 601700
 
5.8%
D 409249
 
3.9%
L 356495
 
3.4%
Other values (12) 1837351
17.7%
Common
ValueCountFrequency (%)
985873
92.4%
( 40274
 
3.8%
) 40274
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11457657
> 99.9%
None 5
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 1430649
12.5%
C 1111639
9.7%
A 1025691
9.0%
O 1020361
8.9%
985873
8.6%
E 973515
8.5%
N 946770
8.3%
S 677821
 
5.9%
R 601700
 
5.3%
D 409249
 
3.6%
Other values (14) 2274389
19.9%
None
ValueCountFrequency (%)
Ó 5
100.0%

MODALIDAD
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct6
Distinct (%)< 0.1%
Missing134696
Missing (%)22.2%
Memory size4.6 MiB
PRESENCIAL
366529 
EN LINEA
75856 
DUAL
 
12128
SEMI-PRESENCIAL
 
8229
DISTANCIA
 
6265

Length

Max length15
Median length10
Mean length9.5750918
Min length4

Characters and Unicode

Total characters4526797
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPRESENCIAL
2nd rowEN LINEA
3rd rowEN LINEA
4th rowPRESENCIAL
5th rowPRESENCIAL

Common Values

ValueCountFrequency (%)
PRESENCIAL 366529
60.3%
EN LINEA 75856
 
12.5%
DUAL 12128
 
2.0%
SEMI-PRESENCIAL 8229
 
1.4%
DISTANCIA 6265
 
1.0%
HIBRIDA 3761
 
0.6%
(Missing) 134696
 
22.2%

Length

2023-03-10T02:48:38.684687image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-10T02:48:38.734823image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
presencial 366529
66.8%
en 75856
 
13.8%
linea 75856
 
13.8%
dual 12128
 
2.2%
semi-presencial 8229
 
1.5%
distancia 6265
 
1.1%
hibrida 3761
 
0.7%

Most occurring characters

ValueCountFrequency (%)
E 909457
20.1%
N 532735
11.8%
A 479033
10.6%
I 478895
10.6%
L 462742
10.2%
S 389252
8.6%
C 381023
8.4%
R 378519
8.4%
P 374758
8.3%
75856
 
1.7%
Other values (7) 64527
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 4442712
98.1%
Space Separator 75856
 
1.7%
Dash Punctuation 8229
 
0.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 909457
20.5%
N 532735
12.0%
A 479033
10.8%
I 478895
10.8%
L 462742
10.4%
S 389252
8.8%
C 381023
8.6%
R 378519
8.5%
P 374758
8.4%
D 22154
 
0.5%
Other values (5) 34144
 
0.8%
Space Separator
ValueCountFrequency (%)
75856
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8229
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4442712
98.1%
Common 84085
 
1.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 909457
20.5%
N 532735
12.0%
A 479033
10.8%
I 478895
10.8%
L 462742
10.4%
S 389252
8.8%
C 381023
8.6%
R 378519
8.5%
P 374758
8.4%
D 22154
 
0.5%
Other values (5) 34144
 
0.8%
Common
ValueCountFrequency (%)
75856
90.2%
- 8229
 
9.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4526797
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 909457
20.1%
N 532735
11.8%
A 479033
10.6%
I 478895
10.6%
L 462742
10.2%
S 389252
8.6%
C 381023
8.4%
R 378519
8.4%
P 374758
8.3%
75856
 
1.7%
Other values (7) 64527
 
1.4%

NIVEL
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct5
Distinct (%)< 0.1%
Missing134696
Missing (%)22.2%
Memory size4.6 MiB
TERCER NIVEL
380123 
TERCER NIVEL TECNOLÓGICO SUPERIOR
68726 
TECNOLOGICO SUPERIOR
 
21132
TERCER NIVEL TÉCNICO SUPERIOR
 
2284
TECNICO SUPERIOR
 
503

Length

Max length33
Median length12
Mean length15.49673
Min length12

Characters and Unicode

Total characters7326358
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTERCER NIVEL TECNOLÓGICO SUPERIOR
2nd rowTERCER NIVEL
3rd rowTERCER NIVEL
4th rowTERCER NIVEL
5th rowTERCER NIVEL TECNOLÓGICO SUPERIOR

Common Values

ValueCountFrequency (%)
TERCER NIVEL 380123
62.6%
TERCER NIVEL TECNOLÓGICO SUPERIOR 68726
 
11.3%
TECNOLOGICO SUPERIOR 21132
 
3.5%
TERCER NIVEL TÉCNICO SUPERIOR 2284
 
0.4%
TECNICO SUPERIOR 503
 
0.1%
(Missing) 134696
 
22.2%

Length

2023-03-10T02:48:38.778902image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-10T02:48:38.825886image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
tercer 451133
41.5%
nivel 451133
41.5%
superior 92645
 
8.5%
tecnológico 68726
 
6.3%
tecnologico 21132
 
1.9%
técnico 2284
 
0.2%
tecnico 503
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
E 1536405
21.0%
R 1087556
14.8%
C 636423
8.7%
I 636423
8.7%
614788
8.4%
T 543778
 
7.4%
N 543778
 
7.4%
L 540991
 
7.4%
V 451133
 
6.2%
O 296280
 
4.0%
Other values (6) 438803
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 6711570
91.6%
Space Separator 614788
 
8.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 1536405
22.9%
R 1087556
16.2%
C 636423
9.5%
I 636423
9.5%
T 543778
 
8.1%
N 543778
 
8.1%
L 540991
 
8.1%
V 451133
 
6.7%
O 296280
 
4.4%
S 92645
 
1.4%
Other values (5) 346158
 
5.2%
Space Separator
ValueCountFrequency (%)
614788
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6711570
91.6%
Common 614788
 
8.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 1536405
22.9%
R 1087556
16.2%
C 636423
9.5%
I 636423
9.5%
T 543778
 
8.1%
N 543778
 
8.1%
L 540991
 
8.1%
V 451133
 
6.7%
O 296280
 
4.4%
S 92645
 
1.4%
Other values (5) 346158
 
5.2%
Common
ValueCountFrequency (%)
614788
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7255348
99.0%
None 71010
 
1.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 1536405
21.2%
R 1087556
15.0%
C 636423
8.8%
I 636423
8.8%
614788
8.5%
T 543778
 
7.5%
N 543778
 
7.5%
L 540991
 
7.5%
V 451133
 
6.2%
O 296280
 
4.1%
Other values (4) 367793
 
5.1%
None
ValueCountFrequency (%)
Ó 68726
96.8%
É 2284
 
3.2%

JORNADA
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)< 0.1%
Missing134696
Missing (%)22.2%
Memory size4.6 MiB
MATUTINA
126226 
INTENSIVA
110304 
NO APLICA JORNADA
94111 
VESPERTINA
83190 
NOCTURNA
58937 

Length

Max length17
Median length10
Mean length10.376817
Min length8

Characters and Unicode

Total characters4905827
Distinct characters17
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVESPERTINA
2nd rowNO APLICA JORNADA
3rd rowNO APLICA JORNADA
4th rowINTENSIVA
5th rowNOCTURNA

Common Values

ValueCountFrequency (%)
MATUTINA 126226
20.8%
INTENSIVA 110304
18.2%
NO APLICA JORNADA 94111
15.5%
VESPERTINA 83190
13.7%
NOCTURNA 58937
9.7%
(Missing) 134696
22.2%

Length

2023-03-10T02:48:38.871760image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-10T02:48:39.054412image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
matutina 126226
19.1%
intensiva 110304
16.7%
no 94111
14.2%
aplica 94111
14.2%
jornada 94111
14.2%
vespertina 83190
12.6%
nocturna 58937
8.9%

Most occurring characters

ValueCountFrequency (%)
A 881327
18.0%
N 736120
15.0%
I 524135
10.7%
T 504883
10.3%
E 276684
 
5.6%
O 247159
 
5.0%
R 236238
 
4.8%
V 193494
 
3.9%
S 193494
 
3.9%
188222
 
3.8%
Other values (7) 924071
18.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 4717605
96.2%
Space Separator 188222
 
3.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 881327
18.7%
N 736120
15.6%
I 524135
11.1%
T 504883
10.7%
E 276684
 
5.9%
O 247159
 
5.2%
R 236238
 
5.0%
V 193494
 
4.1%
S 193494
 
4.1%
U 185163
 
3.9%
Other values (6) 738908
15.7%
Space Separator
ValueCountFrequency (%)
188222
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4717605
96.2%
Common 188222
 
3.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 881327
18.7%
N 736120
15.6%
I 524135
11.1%
T 504883
10.7%
E 276684
 
5.9%
O 247159
 
5.2%
R 236238
 
5.0%
V 193494
 
4.1%
S 193494
 
4.1%
U 185163
 
3.9%
Other values (6) 738908
15.7%
Common
ValueCountFrequency (%)
188222
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4905827
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 881327
18.0%
N 736120
15.0%
I 524135
10.7%
T 504883
10.3%
E 276684
 
5.6%
O 247159
 
5.0%
R 236238
 
4.8%
V 193494
 
3.9%
S 193494
 
3.9%
188222
 
3.8%
Other values (7) 924071
18.8%

ACEPTA_CUPO
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing134696
Missing (%)22.2%
Memory size4.6 MiB
1.0
382769 
0.0
79990 
2.0
 
10009

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1418304
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 382769
63.0%
0.0 79990
 
13.2%
2.0 10009
 
1.6%
(Missing) 134696
 
22.2%

Length

2023-03-10T02:48:39.098181image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-10T02:48:39.140224image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 382769
81.0%
0.0 79990
 
16.9%
2.0 10009
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 552758
39.0%
. 472768
33.3%
1 382769
27.0%
2 10009
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 945536
66.7%
Other Punctuation 472768
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 552758
58.5%
1 382769
40.5%
2 10009
 
1.1%
Other Punctuation
ValueCountFrequency (%)
. 472768
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1418304
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 552758
39.0%
. 472768
33.3%
1 382769
27.0%
2 10009
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1418304
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 552758
39.0%
. 472768
33.3%
1 382769
27.0%
2 10009
 
0.7%

ASA_ESTADO
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing134696
Missing (%)22.2%
Memory size4.6 MiB
1.0
472752 
0.0
 
16

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1418304
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 472752
77.8%
0.0 16
 
< 0.1%
(Missing) 134696
 
22.2%

Length

2023-03-10T02:48:39.176267image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-10T02:48:39.216529image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 472752
> 99.9%
0.0 16
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 472784
33.3%
. 472768
33.3%
1 472752
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 945536
66.7%
Other Punctuation 472768
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 472784
50.0%
1 472752
50.0%
Other Punctuation
ValueCountFrequency (%)
. 472768
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1418304
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 472784
33.3%
. 472768
33.3%
1 472752
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1418304
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 472784
33.3%
. 472768
33.3%
1 472752
33.3%

OFA_ID
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct9052
Distinct (%)1.9%
Missing134696
Missing (%)22.2%
Infinite0
Infinite (%)0.0%
Mean150990.15
Minimum93025
Maximum183438
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2023-03-10T02:48:39.260617image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum93025
5-th percentile95017
Q1151791
median161688
Q3173660
95-th percentile181399
Maximum183438
Range90413
Interquartile range (IQR)21869

Descriptive statistics

Standard deviation30521.793
Coefficient of variation (CV)0.20214427
Kurtosis-0.65400789
Mean150990.15
Median Absolute Deviation (MAD)11024
Skewness-0.99280034
Sum7.1383309 × 1010
Variance9.3157986 × 108
MonotonicityNot monotonic
2023-03-10T02:48:39.315567image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
174128 2156
 
0.4%
159111 1988
 
0.3%
179463 1917
 
0.3%
172714 1899
 
0.3%
174130 1898
 
0.3%
182224 1787
 
0.3%
182232 1648
 
0.3%
179462 1595
 
0.3%
180856 1590
 
0.3%
95019 1537
 
0.3%
Other values (9042) 454753
74.9%
(Missing) 134696
 
22.2%
ValueCountFrequency (%)
93025 142
< 0.1%
93028 234
< 0.1%
93029 81
 
< 0.1%
93034 35
 
< 0.1%
93037 33
 
< 0.1%
93040 41
 
< 0.1%
93041 38
 
< 0.1%
93047 40
 
< 0.1%
93051 44
 
< 0.1%
93054 188
< 0.1%
ValueCountFrequency (%)
183438 224
< 0.1%
183436 37
 
< 0.1%
183433 59
 
< 0.1%
183432 68
 
< 0.1%
183429 67
 
< 0.1%
183425 69
 
< 0.1%
183424 46
 
< 0.1%
183408 70
 
< 0.1%
183402 30
 
< 0.1%
183395 92
< 0.1%

ASA_FECHA_ACEPTACION
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing214686
Missing (%)35.3%
Memory size4.6 MiB

EXONERADO
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing134696
Missing (%)22.2%
Memory size1.2 MiB
False
472768 
(Missing)
134696 
ValueCountFrequency (%)
False 472768
77.8%
(Missing) 134696
 
22.2%
2023-03-10T02:48:39.365437image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

OFA_ESTADO
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing134696
Missing (%)22.2%
Memory size4.6 MiB
1.0
472768 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1418304
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 472768
77.8%
(Missing) 134696
 
22.2%

Length

2023-03-10T02:48:39.397861image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-10T02:48:39.437658image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 472768
100.0%

Most occurring characters

ValueCountFrequency (%)
1 472768
33.3%
. 472768
33.3%
0 472768
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 945536
66.7%
Other Punctuation 472768
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 472768
50.0%
0 472768
50.0%
Other Punctuation
ValueCountFrequency (%)
. 472768
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1418304
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 472768
33.3%
. 472768
33.3%
0 472768
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1418304
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 472768
33.3%
. 472768
33.3%
0 472768
33.3%

ASA_EXONERA
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing134696
Missing (%)22.2%
Memory size4.6 MiB
0.0
472768 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1418304
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 472768
77.8%
(Missing) 134696
 
22.2%

Length

2023-03-10T02:48:39.470698image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-10T02:48:39.509296image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 472768
100.0%

Most occurring characters

ValueCountFrequency (%)
0 945536
66.7%
. 472768
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 945536
66.7%
Other Punctuation 472768
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 945536
100.0%
Other Punctuation
ValueCountFrequency (%)
. 472768
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1418304
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 945536
66.7%
. 472768
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1418304
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 945536
66.7%
. 472768
33.3%

PER_ID
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing134696
Missing (%)22.2%
Memory size4.6 MiB
22.0
133235 
20.0
122520 
18.0
112037 
21.0
104976 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters1891072
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row22.0
2nd row22.0
3rd row22.0
4th row22.0
5th row22.0

Common Values

ValueCountFrequency (%)
22.0 133235
21.9%
20.0 122520
20.2%
18.0 112037
18.4%
21.0 104976
17.3%
(Missing) 134696
22.2%

Length

2023-03-10T02:48:39.541899image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-10T02:48:39.586056image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
22.0 133235
28.2%
20.0 122520
25.9%
18.0 112037
23.7%
21.0 104976
22.2%

Most occurring characters

ValueCountFrequency (%)
0 595288
31.5%
2 493966
26.1%
. 472768
25.0%
1 217013
 
11.5%
8 112037
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1418304
75.0%
Other Punctuation 472768
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 595288
42.0%
2 493966
34.8%
1 217013
 
15.3%
8 112037
 
7.9%
Other Punctuation
ValueCountFrequency (%)
. 472768
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1891072
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 595288
31.5%
2 493966
26.1%
. 472768
25.0%
1 217013
 
11.5%
8 112037
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1891072
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 595288
31.5%
2 493966
26.1%
. 472768
25.0%
1 217013
 
11.5%
8 112037
 
5.9%

SEGMENTO
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct46
Distinct (%)< 0.1%
Missing134696
Missing (%)22.2%
Memory size4.6 MiB
POBLACION GENERAL
190039 
/POLITICA DE ACCION AFIRMATIVA
166106 
/POBLACION GENERAL
60470 
POLITICA DE ACCION AFIRMATIVA
39671 
/POBLACION GENERAL/No aceptó cupo 1ra opción de Carrera
 
4628
Other values (41)
 
11854

Length

Max length120
Median length101
Mean length23.887211
Min length3

Characters and Unicode

Total characters11293109
Distinct characters39
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st row/POLITICA DE ACCION AFIRMATIVA
2nd rowPOBLACION GENERAL
3rd rowPOBLACION GENERAL
4th row/POLITICA DE ACCION AFIRMATIVA
5th row/POLITICA DE ACCION AFIRMATIVA

Common Values

ValueCountFrequency (%)
POBLACION GENERAL 190039
31.3%
/POLITICA DE ACCION AFIRMATIVA 166106
27.3%
/POBLACION GENERAL 60470
 
10.0%
POLITICA DE ACCION AFIRMATIVA 39671
 
6.5%
/POBLACION GENERAL/No aceptó cupo 1ra opción de Carrera 4628
 
0.8%
POLITICA DE ACCION AFIRMATIVA/No aceptó cupo 1ra opción de Carrera 4134
 
0.7%
/MERITO TERRITORIAL/POLITICA DE ACCION AFIRMATIVA 1399
 
0.2%
/MERITO TERRITORIAL 1154
 
0.2%
DUAL FOCALIZADO FUERZA TERRESTRE 900
 
0.1%
DUAL FOCALIZADO MIES 548
 
0.1%
Other values (36) 3719
 
0.6%
(Missing) 134696
22.2%

Length

2023-03-10T02:48:39.639076image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
poblacion 256332
17.9%
general 250509
17.5%
de 221893
15.5%
accion 212811
14.8%
politica 211298
14.7%
afirmativa 207290
14.4%
aceptó 8961
 
0.6%
cupo 8961
 
0.6%
1ra 8961
 
0.6%
opción 8961
 
0.6%
Other values (44) 39580
 
2.8%

Most occurring characters

ValueCountFrequency (%)
A 1597870
14.1%
I 1337571
11.8%
963539
8.5%
O 954583
8.5%
C 909586
 
8.1%
E 740645
 
6.6%
L 738251
 
6.5%
N 736032
 
6.5%
R 490142
 
4.3%
P 470303
 
4.2%
Other values (29) 2354587
20.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 9811918
86.9%
Space Separator 963539
 
8.5%
Other Punctuation 248332
 
2.2%
Lowercase Letter 224270
 
2.0%
Math Symbol 36089
 
0.3%
Decimal Number 8961
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 1597870
16.3%
I 1337571
13.6%
O 954583
9.7%
C 909586
9.3%
E 740645
7.5%
L 738251
7.5%
N 736032
7.5%
R 490142
 
5.0%
P 470303
 
4.8%
T 438478
 
4.5%
Other values (11) 1398457
14.3%
Lowercase Letter
ValueCountFrequency (%)
r 35844
16.0%
a 35844
16.0%
c 26883
12.0%
o 26883
12.0%
p 26883
12.0%
e 26883
12.0%
d 8961
 
4.0%
n 8961
 
4.0%
i 8961
 
4.0%
u 8961
 
4.0%
Other values (3) 9206
 
4.1%
Math Symbol
ValueCountFrequency (%)
18167
50.3%
17922
49.7%
Space Separator
ValueCountFrequency (%)
963539
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 248332
100.0%
Decimal Number
ValueCountFrequency (%)
1 8961
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10036188
88.9%
Common 1256921
 
11.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 1597870
15.9%
I 1337571
13.3%
O 954583
9.5%
C 909586
9.1%
E 740645
7.4%
L 738251
7.4%
N 736032
7.3%
R 490142
 
4.9%
P 470303
 
4.7%
T 438478
 
4.4%
Other values (24) 1622727
16.2%
Common
ValueCountFrequency (%)
963539
76.7%
/ 248332
 
19.8%
18167
 
1.4%
17922
 
1.4%
1 8961
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11256636
99.7%
Math Operators 36089
 
0.3%
None 384
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 1597870
14.2%
I 1337571
11.9%
963539
8.6%
O 954583
8.5%
C 909586
 
8.1%
E 740645
 
6.6%
L 738251
 
6.6%
N 736032
 
6.5%
R 490142
 
4.4%
P 470303
 
4.2%
Other values (24) 2318114
20.6%
Math Operators
ValueCountFrequency (%)
18167
50.3%
17922
49.7%
None
ValueCountFrequency (%)
â 230
59.9%
É 139
36.2%
ì 15
 
3.9%

TIPO_CUPO
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing134696
Missing (%)22.2%
Memory size4.6 MiB
NIVELACIÓN
258224 
PRIMER SEMESTRE
134828 
NIVELACIÓN
79716 

Length

Max length15
Median length10
Mean length11.594558
Min length10

Characters and Unicode

Total characters5481536
Distinct characters16
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPRIMER SEMESTRE
2nd rowNIVELACIÓN
3rd rowNIVELACIÓN
4th rowPRIMER SEMESTRE
5th rowPRIMER SEMESTRE

Common Values

ValueCountFrequency (%)
NIVELACIÓN 258224
42.5%
PRIMER SEMESTRE 134828
22.2%
NIVELACIÓN 79716
 
13.1%
(Missing) 134696
22.2%

Length

2023-03-10T02:48:39.691032image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-10T02:48:39.737964image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
nivelación 258224
42.5%
primer 134828
22.2%
semestre 134828
22.2%
nivelaciÓn 79716
 
13.1%

Most occurring characters

ValueCountFrequency (%)
E 877252
16.0%
I 810708
14.8%
N 675880
12.3%
R 404484
7.4%
V 337940
 
6.2%
L 337940
 
6.2%
A 337940
 
6.2%
C 337940
 
6.2%
M 269656
 
4.9%
S 269656
 
4.9%
Other values (6) 822140
15.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 5187276
94.6%
Space Separator 134828
 
2.5%
Math Symbol 79716
 
1.5%
Lowercase Letter 79716
 
1.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 877252
16.9%
I 810708
15.6%
N 675880
13.0%
R 404484
7.8%
V 337940
 
6.5%
L 337940
 
6.5%
A 337940
 
6.5%
C 337940
 
6.5%
M 269656
 
5.2%
S 269656
 
5.2%
Other values (3) 527880
10.2%
Space Separator
ValueCountFrequency (%)
134828
100.0%
Math Symbol
ValueCountFrequency (%)
79716
100.0%
Lowercase Letter
ValueCountFrequency (%)
ì 79716
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5266992
96.1%
Common 214544
 
3.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 877252
16.7%
I 810708
15.4%
N 675880
12.8%
R 404484
7.7%
V 337940
 
6.4%
L 337940
 
6.4%
A 337940
 
6.4%
C 337940
 
6.4%
M 269656
 
5.1%
S 269656
 
5.1%
Other values (4) 607596
11.5%
Common
ValueCountFrequency (%)
134828
62.8%
79716
37.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5063880
92.4%
None 337940
 
6.2%
Math Operators 79716
 
1.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 877252
17.3%
I 810708
16.0%
N 675880
13.3%
R 404484
8.0%
V 337940
 
6.7%
L 337940
 
6.7%
A 337940
 
6.7%
C 337940
 
6.7%
M 269656
 
5.3%
S 269656
 
5.3%
Other values (3) 404484
8.0%
None
ValueCountFrequency (%)
Ó 258224
76.4%
ì 79716
 
23.6%
Math Operators
ValueCountFrequency (%)
79716
100.0%

DISCAPACIDAD
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing134696
Missing (%)22.2%
Memory size4.6 MiB
NO
468966 
SI
 
3802

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters945536
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNO
2nd rowNO
3rd rowNO
4th rowNO
5th rowNO

Common Values

ValueCountFrequency (%)
NO 468966
77.2%
SI 3802
 
0.6%
(Missing) 134696
 
22.2%

Length

2023-03-10T02:48:39.776404image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-10T02:48:39.816903image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
no 468966
99.2%
si 3802
 
0.8%

Most occurring characters

ValueCountFrequency (%)
N 468966
49.6%
O 468966
49.6%
S 3802
 
0.4%
I 3802
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 945536
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 468966
49.6%
O 468966
49.6%
S 3802
 
0.4%
I 3802
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 945536
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 468966
49.6%
O 468966
49.6%
S 3802
 
0.4%
I 3802
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 945536
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 468966
49.6%
O 468966
49.6%
S 3802
 
0.4%
I 3802
 
0.4%

ASA_BECA
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing134696
Missing (%)22.2%
Memory size4.6 MiB
0.0
472768 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1418304
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 472768
77.8%
(Missing) 134696
 
22.2%

Length

2023-03-10T02:48:39.849767image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-10T02:48:39.889213image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 472768
100.0%

Most occurring characters

ValueCountFrequency (%)
0 945536
66.7%
. 472768
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 945536
66.7%
Other Punctuation 472768
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 945536
100.0%
Other Punctuation
ValueCountFrequency (%)
. 472768
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1418304
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 945536
66.7%
. 472768
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1418304
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 945536
66.7%
. 472768
33.3%

PRD_ID_NUM_POSTULACION
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct20
Distinct (%)< 0.1%
Missing134696
Missing (%)22.2%
Infinite0
Infinite (%)0.0%
Mean475.29289
Minimum446
Maximum1092
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2023-03-10T02:48:39.920280image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum446
5-th percentile446
Q1446
median446
Q3446
95-th percentile494
Maximum1092
Range646
Interquartile range (IQR)0

Descriptive statistics

Standard deviation98.207119
Coefficient of variation (CV)0.20662442
Kurtosis22.513432
Mean475.29289
Median Absolute Deviation (MAD)0
Skewness4.732605
Sum2.2470327 × 108
Variance9644.6382
MonotonicityNot monotonic
2023-03-10T02:48:39.962295image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
446 358070
58.9%
492 67017
 
11.0%
494 26482
 
4.4%
908 10047
 
1.7%
1092 5017
 
0.8%
808 2058
 
0.3%
770 1651
 
0.3%
471 847
 
0.1%
561 685
 
0.1%
769 369
 
0.1%
Other values (10) 525
 
0.1%
(Missing) 134696
 
22.2%
ValueCountFrequency (%)
446 358070
58.9%
471 847
 
0.1%
492 67017
 
11.0%
494 26482
 
4.4%
561 685
 
0.1%
564 253
 
< 0.1%
565 68
 
< 0.1%
569 6
 
< 0.1%
580 6
 
< 0.1%
726 5
 
< 0.1%
ValueCountFrequency (%)
1092 5017
0.8%
908 10047
1.7%
874 91
 
< 0.1%
823 1
 
< 0.1%
808 2058
 
0.3%
806 54
 
< 0.1%
774 26
 
< 0.1%
772 15
 
< 0.1%
770 1651
 
0.3%
769 369
 
0.1%

PRD_ID_NUM_ASIGNACION
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct22
Distinct (%)< 0.1%
Missing134696
Missing (%)22.2%
Infinite0
Infinite (%)0.0%
Mean479.00899
Minimum446
Maximum1092
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2023-03-10T02:48:40.008135image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum446
5-th percentile446
Q1446
median446
Q3463
95-th percentile531
Maximum1092
Range646
Interquartile range (IQR)17

Descriptive statistics

Standard deviation98.356332
Coefficient of variation (CV)0.20533295
Kurtosis21.657578
Mean479.00899
Median Absolute Deviation (MAD)0
Skewness4.6084108
Sum2.2646012 × 108
Variance9673.968
MonotonicityNot monotonic
2023-03-10T02:48:40.050532image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
446 302948
49.9%
463 55122
 
9.1%
492 45997
 
7.6%
494 26482
 
4.4%
531 21020
 
3.5%
908 10047
 
1.7%
1092 5017
 
0.8%
808 2058
 
0.3%
770 1651
 
0.3%
471 847
 
0.1%
Other values (12) 1579
 
0.3%
(Missing) 134696
22.2%
ValueCountFrequency (%)
446 302948
49.9%
463 55122
 
9.1%
471 847
 
0.1%
492 45997
 
7.6%
494 26482
 
4.4%
531 21020
 
3.5%
561 685
 
0.1%
564 253
 
< 0.1%
565 68
 
< 0.1%
569 6
 
< 0.1%
ValueCountFrequency (%)
1092 5017
0.8%
908 10047
1.7%
874 91
 
< 0.1%
823 1
 
< 0.1%
808 2058
 
0.3%
806 54
 
< 0.1%
774 26
 
< 0.1%
772 15
 
< 0.1%
770 1651
 
0.3%
769 369
 
0.1%

INSTANCIA_POSTULACION
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct24
Distinct (%)< 0.1%
Missing134696
Missing (%)22.2%
Memory size4.6 MiB
PRIMERA POSTULACION
276947 
PRIMERA POSTULACION
81123 
SEGUNDA POSTULACIÓN
47796 
SEGUNDA POSTULACIÓN
 
19221
TERCERA POSTULACION
 
14599
Other values (19)
33082 

Length

Max length83
Median length19
Mean length19.914567
Min length18

Characters and Unicode

Total characters9414970
Distinct characters33
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowPRIMERA POSTULACION
2nd rowPRIMERA POSTULACION
3rd rowPRIMERA POSTULACION
4th rowPRIMERA POSTULACION
5th rowPRIMERA POSTULACION

Common Values

ValueCountFrequency (%)
PRIMERA POSTULACION 276947
45.6%
PRIMERA POSTULACION 81123
 
13.4%
SEGUNDA POSTULACIÓN 47796
 
7.9%
SEGUNDA POSTULACIÓN 19221
 
3.2%
TERCERA POSTULACION 14599
 
2.4%
TERCERA POSTULACIÓN 11883
 
2.0%
POSTULACION AUTO ASIGNACION DE CUPO 10047
 
1.7%
CUARTA POSTULACIÓN 5017
 
0.8%
ASIGNACIÓN DE CUPO EN LÍNEA 2058
 
0.3%
REGISTRO EN EL SISTEMA DE CUPOS DE DUAL FOCALIZADO FUERZA TERRESTRE 1651
 
0.3%
Other values (14) 2426
 
0.4%
(Missing) 134696
22.2%

Length

2023-03-10T02:48:40.102903image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
postulacion 382716
37.8%
primera 358070
35.4%
segunda 67017
 
6.6%
postulación 64696
 
6.4%
tercera 26482
 
2.6%
postulaciÓn 19221
 
1.9%
de 18890
 
1.9%
cupo 12105
 
1.2%
asignacion 10732
 
1.1%
auto 10047
 
1.0%
Other values (50) 42928
 
4.2%

Most occurring characters

ValueCountFrequency (%)
A 983966
10.5%
O 896589
9.5%
I 863886
 
9.2%
P 840747
 
8.9%
R 789400
 
8.4%
621259
 
6.6%
U 569776
 
6.1%
N 568184
 
6.0%
S 563621
 
6.0%
C 531293
 
5.6%
Other values (23) 2186249
23.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 8746507
92.9%
Space Separator 621259
 
6.6%
Math Symbol 23581
 
0.3%
Lowercase Letter 23581
 
0.3%
Other Punctuation 18
 
< 0.1%
Open Punctuation 12
 
< 0.1%
Close Punctuation 12
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 983966
11.2%
O 896589
10.3%
I 863886
9.9%
P 840747
9.6%
R 789400
9.0%
U 569776
 
6.5%
N 568184
 
6.5%
S 563621
 
6.4%
C 531293
 
6.1%
E 525563
 
6.0%
Other values (14) 1613482
18.4%
Lowercase Letter
ValueCountFrequency (%)
ì 21287
90.3%
ç 2064
 
8.8%
â 230
 
1.0%
Other Punctuation
ValueCountFrequency (%)
: 13
72.2%
. 5
 
27.8%
Space Separator
ValueCountFrequency (%)
621259
100.0%
Math Symbol
ValueCountFrequency (%)
23581
100.0%
Open Punctuation
ValueCountFrequency (%)
( 12
100.0%
Close Punctuation
ValueCountFrequency (%)
) 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8770088
93.2%
Common 644882
 
6.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 983966
11.2%
O 896589
10.2%
I 863886
9.9%
P 840747
9.6%
R 789400
9.0%
U 569776
 
6.5%
N 568184
 
6.5%
S 563621
 
6.4%
C 531293
 
6.1%
E 525563
 
6.0%
Other values (17) 1637063
18.7%
Common
ValueCountFrequency (%)
621259
96.3%
23581
 
3.7%
: 13
 
< 0.1%
( 12
 
< 0.1%
) 12
 
< 0.1%
. 5
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9302973
98.8%
None 88416
 
0.9%
Math Operators 23581
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 983966
10.6%
O 896589
9.6%
I 863886
9.3%
P 840747
9.0%
R 789400
 
8.5%
621259
 
6.7%
U 569776
 
6.1%
N 568184
 
6.1%
S 563621
 
6.1%
C 531293
 
5.7%
Other values (17) 2074252
22.3%
None
ValueCountFrequency (%)
Ó 64696
73.2%
ì 21287
 
24.1%
ç 2064
 
2.3%
â 230
 
0.3%
É 139
 
0.2%
Math Operators
ValueCountFrequency (%)
23581
100.0%

INSTANCIA_ASIGNACION
Categorical

HIGH CORRELATION  MISSING 

Distinct28
Distinct (%)< 0.1%
Missing134696
Missing (%)22.2%
Memory size4.6 MiB
PRIMERA ASIGNACIÓN DE CUPOS
235043 
PRIMERA ASIGNACIÓN DE CUPOS
67905 
SEGUNDA ASIGNACIÓN DE CUPOS
41904 
TERCERA ASIGNACIÓN DE CUPOS
32880 
QUINTA ASIGNACIÓN DE CUPOS
 
18925
Other values (23)
76111 

Length

Max length83
Median length27
Mean length27.545638
Min length25

Characters and Unicode

Total characters13022696
Distinct characters35
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowPRIMERA ASIGNACIÓN DE CUPOS
2nd rowSEGUNDA ASIGNACIÓN DE CUPOS
3rd rowPRIMERA ASIGNACIÓN DE CUPOS
4th rowPRIMERA ASIGNACIÓN DE CUPOS
5th rowPRIMERA ASIGNACIÓN DE CUPOS

Common Values

ValueCountFrequency (%)
PRIMERA ASIGNACIÓN DE CUPOS 235043
38.7%
PRIMERA ASIGNACIÓN DE CUPOS 67905
 
11.2%
SEGUNDA ASIGNACIÓN DE CUPOS 41904
 
6.9%
TERCERA ASIGNACIÓN DE CUPOS 32880
 
5.4%
QUINTA ASIGNACIÓN DE CUPOS 18925
 
3.1%
CUARTA ASIGNACIÓN DE CUPOS 14916
 
2.5%
SEGUNDA ASIGNACIÓN DE CUPOS 13218
 
2.2%
TERCERA ASIGNACIÓN DE CUPOS 13117
 
2.2%
POSTULACION AUTO ASIGNACION DE CUPO 10047
 
1.7%
QUINTA ASIGNACIÓN DE CUPOS 7557
 
1.2%
Other values (18) 17256
 
2.8%
(Missing) 134696
22.2%

Length

2023-03-10T02:48:40.153882image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de 475476
24.7%
cupos 459965
23.9%
asignación 348685
18.1%
primera 302948
15.7%
asignaciÓn 109959
 
5.7%
segunda 55122
 
2.9%
tercera 45997
 
2.4%
quinta 26482
 
1.4%
cuarta 21020
 
1.1%
cupo 12105
 
0.6%
Other values (51) 68317
 
3.5%

Most occurring characters

ValueCountFrequency (%)
A 1456555
11.2%
1453308
11.2%
I 1291832
9.9%
N 1039357
 
8.0%
C 1023397
 
7.9%
S 1013329
 
7.8%
E 959179
 
7.4%
P 785625
 
6.0%
R 734189
 
5.6%
U 600366
 
4.6%
Other values (25) 2665559
20.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 11344824
87.1%
Space Separator 1453308
 
11.2%
Math Symbol 112261
 
0.9%
Lowercase Letter 112261
 
0.9%
Other Punctuation 18
 
< 0.1%
Open Punctuation 12
 
< 0.1%
Close Punctuation 12
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 1456555
12.8%
I 1291832
11.4%
N 1039357
9.2%
C 1023397
9.0%
S 1013329
8.9%
E 959179
8.5%
P 785625
6.9%
R 734189
 
6.5%
U 600366
 
5.3%
D 538087
 
4.7%
Other values (16) 1902908
16.8%
Lowercase Letter
ValueCountFrequency (%)
ì 109967
98.0%
ç 2064
 
1.8%
â 230
 
0.2%
Other Punctuation
ValueCountFrequency (%)
: 13
72.2%
. 5
 
27.8%
Space Separator
ValueCountFrequency (%)
1453308
100.0%
Math Symbol
ValueCountFrequency (%)
112261
100.0%
Open Punctuation
ValueCountFrequency (%)
( 12
100.0%
Close Punctuation
ValueCountFrequency (%)
) 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11457085
88.0%
Common 1565611
 
12.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 1456555
12.7%
I 1291832
11.3%
N 1039357
9.1%
C 1023397
8.9%
S 1013329
8.8%
E 959179
8.4%
P 785625
 
6.9%
R 734189
 
6.4%
U 600366
 
5.2%
D 538087
 
4.7%
Other values (19) 2015169
17.6%
Common
ValueCountFrequency (%)
1453308
92.8%
112261
 
7.2%
: 13
 
< 0.1%
( 12
 
< 0.1%
) 12
 
< 0.1%
. 5
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12449350
95.6%
None 461085
 
3.5%
Math Operators 112261
 
0.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 1456555
11.7%
1453308
11.7%
I 1291832
10.4%
N 1039357
8.3%
C 1023397
8.2%
S 1013329
8.1%
E 959179
7.7%
P 785625
 
6.3%
R 734189
 
5.9%
U 600366
 
4.8%
Other values (19) 2092213
16.8%
None
ValueCountFrequency (%)
Ó 348685
75.6%
ì 109967
 
23.8%
ç 2064
 
0.4%
â 230
 
< 0.1%
É 139
 
< 0.1%
Math Operators
ValueCountFrequency (%)
112261
100.0%

ASA_OBSERVACION
Categorical

HIGH CARDINALITY  IMBALANCE  MISSING 

Distinct3418
Distinct (%)0.7%
Missing134696
Missing (%)22.2%
Memory size4.6 MiB
1era Asignación de cupos 1era Postulación P22 de acuerdo a las reglas remitidas en el RF CGTIC-PROY-2021-RF-073
86872 
1era Asignación de cupos 1era Postulación P20 de acuerdo a las reglas remitidas en el RF CGTIC-PROY-2020-RF-075
75923 
1era Asignación de cupos 1era Postulación P21 de acuerdo a las reglas remitidas en el RF CGTIC-PROY-2021-RF-025-A
72248 
1era Asignación de cupos 1era Postulación P18 de acuerdo a las reglas remitidas en el RF CGTIC-PROY-2019-RF-104 y memorando Nro.SENESCYT-SAES-DDA-2019-0456-M del 14 septiembre 2019
67891 
2da Asignación de cupos 1era Postulación p22 de acuerdo a las reglas remitidas en el RF CGTIC-PROY-2021-RF-075
15920 
Other values (3413)
153914 

Length

Max length393
Median length184
Mean length126.25273
Min length82

Characters and Unicode

Total characters59688253
Distinct characters61
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3392 ?
Unique (%)0.7%

Sample

1st row1era Asignación de cupos 1era Postulación P22 de acuerdo a las reglas remitidas en el RF CGTIC-PROY-2021-RF-073
2nd row2da Asignación de cupos 1era Postulación p22 de acuerdo a las reglas remitidas en el RF CGTIC-PROY-2021-RF-075
3rd row1era Asignación de cupos 1era Postulación P22 de acuerdo a las reglas remitidas en el RF CGTIC-PROY-2021-RF-073
4th row1era Asignación de cupos 1era Postulación P22 de acuerdo a las reglas remitidas en el RF CGTIC-PROY-2021-RF-073
5th row1era Asignación de cupos 1era Postulación P22 de acuerdo a las reglas remitidas en el RF CGTIC-PROY-2021-RF-073

Common Values

ValueCountFrequency (%)
1era Asignación de cupos 1era Postulación P22 de acuerdo a las reglas remitidas en el RF CGTIC-PROY-2021-RF-073 86872
14.3%
1era Asignación de cupos 1era Postulación P20 de acuerdo a las reglas remitidas en el RF CGTIC-PROY-2020-RF-075 75923
12.5%
1era Asignación de cupos 1era Postulación P21 de acuerdo a las reglas remitidas en el RF CGTIC-PROY-2021-RF-025-A 72248
11.9%
1era Asignación de cupos 1era Postulación P18 de acuerdo a las reglas remitidas en el RF CGTIC-PROY-2019-RF-104 y memorando Nro.SENESCYT-SAES-DDA-2019-0456-M del 14 septiembre 2019 67891
11.2%
2da Asignación de cupos 1era Postulación p22 de acuerdo a las reglas remitidas en el RF CGTIC-PROY-2021-RF-075 15920
 
2.6%
2da Asignación de cupos 1era Postulación P20 de acuerdo a las reglas remitidas en el RF CGTIC-PROY-2020-RF-078 14889
 
2.5%
2da Asignación de cupos 1era Postulación P18 de acuerdo a las reglas remitidas en el RF CGTIC-PROY-2019-RF-106 y memorando Nro. SENESCYT-SAES-DDA-2019-0465-M del 20/08/2019 13216
 
2.2%
2da Asignación de cupos 2da Postulación P18 de acuerdo a las reglas remitidas en el RF CGTIC-PROY-2019-RF-110 y memorando Nro. SENESCYT-SAES-DDA-2019-0492-M del 03/09/2019 13117
 
2.2%
3ra Asignación de cupos 2da Postulación P22 de acuerdo a las reglas remitidas en el CGTIC-PROY-2022-RF-081 12813
 
2.1%
3ra Asignación de cupos 2da Postulación P20 de acuerdo a las reglas remitidas en el CGTIC-PROY-2020-RF-082 11558
 
1.9%
Other values (3408) 88321
14.5%
(Missing) 134696
22.2%

Length

2023-03-10T02:48:40.205213image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de 929354
 
10.7%
1era 661018
 
7.6%
en 462036
 
5.3%
el 456618
 
5.3%
cupos 456586
 
5.3%
remitidas 456586
 
5.3%
acuerdo 456586
 
5.3%
a 456586
 
5.3%
las 456586
 
5.3%
reglas 456586
 
5.3%
Other values (3484) 3415624
39.4%

Most occurring characters

ValueCountFrequency (%)
8191398
 
13.7%
e 4362740
 
7.3%
a 4331565
 
7.3%
s 2900691
 
4.9%
- 2537131
 
4.3%
i 2417193
 
4.0%
r 2399060
 
4.0%
d 2208593
 
3.7%
n 2037157
 
3.4%
l 1964783
 
3.3%
Other values (51) 26337942
44.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 32020135
53.6%
Uppercase Letter 9439770
 
15.8%
Space Separator 8191398
 
13.7%
Decimal Number 6839971
 
11.5%
Dash Punctuation 2537131
 
4.3%
Math Symbol 456484
 
0.8%
Other Punctuation 196564
 
0.3%
Connector Punctuation 6800
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 4362740
13.6%
a 4331565
13.5%
s 2900691
9.1%
i 2417193
7.5%
r 2399060
7.5%
d 2208593
 
6.9%
n 2037157
 
6.4%
l 1964783
 
6.1%
c 1889191
 
5.9%
o 1773351
 
5.5%
Other values (13) 5735811
17.9%
Uppercase Letter
ValueCountFrequency (%)
R 1377350
14.6%
P 1368301
14.5%
C 1054874
11.2%
F 904534
9.6%
A 759777
8.0%
T 583476
6.2%
Y 579367
6.1%
I 492933
 
5.2%
O 472816
 
5.0%
G 472784
 
5.0%
Other values (7) 1373558
14.6%
Decimal Number
ValueCountFrequency (%)
2 1880305
27.5%
1 1668531
24.4%
0 1612514
23.6%
9 404894
 
5.9%
5 296565
 
4.3%
4 264382
 
3.9%
7 202723
 
3.0%
8 197573
 
2.9%
3 194435
 
2.8%
6 118049
 
1.7%
Math Symbol
ValueCountFrequency (%)
228242
50.0%
222032
48.6%
4152
 
0.9%
2058
 
0.5%
Other Punctuation
ValueCountFrequency (%)
. 111325
56.6%
/ 73086
37.2%
, 12137
 
6.2%
: 16
 
< 0.1%
Space Separator
ValueCountFrequency (%)
8191398
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2537131
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 6800
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 41459905
69.5%
Common 18228348
30.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 4362740
 
10.5%
a 4331565
 
10.4%
s 2900691
 
7.0%
i 2417193
 
5.8%
r 2399060
 
5.8%
d 2208593
 
5.3%
n 2037157
 
4.9%
l 1964783
 
4.7%
c 1889191
 
4.6%
o 1773351
 
4.3%
Other values (30) 15175581
36.6%
Common
ValueCountFrequency (%)
8191398
44.9%
- 2537131
 
13.9%
2 1880305
 
10.3%
1 1668531
 
9.2%
0 1612514
 
8.8%
9 404894
 
2.2%
5 296565
 
1.6%
4 264382
 
1.5%
228242
 
1.3%
222032
 
1.2%
Other values (11) 922354
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 58508308
98.0%
None 723461
 
1.2%
Math Operators 456484
 
0.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8191398
 
14.0%
e 4362740
 
7.5%
a 4331565
 
7.4%
s 2900691
 
5.0%
- 2537131
 
4.3%
i 2417193
 
4.1%
r 2399060
 
4.1%
d 2208593
 
3.8%
n 2037157
 
3.5%
l 1964783
 
3.4%
Other values (45) 25157997
43.0%
None
ValueCountFrequency (%)
ó 721462
99.7%
ú 1999
 
0.3%
Math Operators
ValueCountFrequency (%)
228242
50.0%
222032
48.6%
4152
 
0.9%
2058
 
0.5%

ASA_GRATUIDAD
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct5
Distinct (%)< 0.1%
Missing246733
Missing (%)40.6%
Memory size4.6 MiB
0.0
340789 
2.0
 
10232
4.0
 
7005
3.0
 
2422
5.0
 
283

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1082193
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row4.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 340789
56.1%
2.0 10232
 
1.7%
4.0 7005
 
1.2%
3.0 2422
 
0.4%
5.0 283
 
< 0.1%
(Missing) 246733
40.6%

Length

2023-03-10T02:48:40.253023image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-10T02:48:40.302517image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 340789
94.5%
2.0 10232
 
2.8%
4.0 7005
 
1.9%
3.0 2422
 
0.7%
5.0 283
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 701520
64.8%
. 360731
33.3%
2 10232
 
0.9%
4 7005
 
0.6%
3 2422
 
0.2%
5 283
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 721462
66.7%
Other Punctuation 360731
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 701520
97.2%
2 10232
 
1.4%
4 7005
 
1.0%
3 2422
 
0.3%
5 283
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 360731
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1082193
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 701520
64.8%
. 360731
33.3%
2 10232
 
0.9%
4 7005
 
0.6%
3 2422
 
0.2%
5 283
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1082193
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 701520
64.8%
. 360731
33.3%
2 10232
 
0.9%
4 7005
 
0.6%
3 2422
 
0.2%
5 283
 
< 0.1%

GRATUIDAD
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct5
Distinct (%)< 0.1%
Missing253300
Missing (%)41.7%
Memory size4.6 MiB
MANTIENE GRATUIDAD
340863 
SIN GRATUIDAD
 
10153
NO APLICA
 
2422
SEGUNDA CARRERA
 
443
SEGUNDA CARRERA IES PARTICULAR
 
283

Length

Max length30
Median length18
Mean length17.800951
Min length9

Characters and Unicode

Total characters6304456
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMANTIENE GRATUIDAD
2nd rowMANTIENE GRATUIDAD
3rd rowMANTIENE GRATUIDAD
4th rowMANTIENE GRATUIDAD
5th rowMANTIENE GRATUIDAD

Common Values

ValueCountFrequency (%)
MANTIENE GRATUIDAD 340863
56.1%
SIN GRATUIDAD 10153
 
1.7%
NO APLICA 2422
 
0.4%
SEGUNDA CARRERA 443
 
0.1%
SEGUNDA CARRERA IES PARTICULAR 283
 
< 0.1%
(Missing) 253300
41.7%

Length

2023-03-10T02:48:40.345476image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-10T02:48:40.398248image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
gratuidad 351016
49.5%
mantiene 340863
48.1%
sin 10153
 
1.4%
no 2422
 
0.3%
aplica 2422
 
0.3%
segunda 726
 
0.1%
carrera 726
 
0.1%
ies 283
 
< 0.1%
particular 283
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 1050483
16.7%
I 705020
11.2%
D 702758
11.1%
N 695027
11.0%
T 692162
11.0%
E 683461
10.8%
354730
 
5.6%
R 353760
 
5.6%
U 352025
 
5.6%
G 351742
 
5.6%
Other values (6) 363288
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 5949726
94.4%
Space Separator 354730
 
5.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 1050483
17.7%
I 705020
11.8%
D 702758
11.8%
N 695027
11.7%
T 692162
11.6%
E 683461
11.5%
R 353760
 
5.9%
U 352025
 
5.9%
G 351742
 
5.9%
M 340863
 
5.7%
Other values (5) 22425
 
0.4%
Space Separator
ValueCountFrequency (%)
354730
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5949726
94.4%
Common 354730
 
5.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 1050483
17.7%
I 705020
11.8%
D 702758
11.8%
N 695027
11.7%
T 692162
11.6%
E 683461
11.5%
R 353760
 
5.9%
U 352025
 
5.9%
G 351742
 
5.9%
M 340863
 
5.7%
Other values (5) 22425
 
0.4%
Common
ValueCountFrequency (%)
354730
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6304456
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 1050483
16.7%
I 705020
11.2%
D 702758
11.1%
N 695027
11.0%
T 692162
11.0%
E 683461
10.8%
354730
 
5.6%
R 353760
 
5.6%
U 352025
 
5.6%
G 351742
 
5.6%
Other values (6) 363288
 
5.8%

ACEPTA_CUPO_DDA
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing246733
Missing (%)40.6%
Memory size4.6 MiB
1.0
295865 
0.0
57204 
2.0
 
7662

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1082193
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 295865
48.7%
0.0 57204
 
9.4%
2.0 7662
 
1.3%
(Missing) 246733
40.6%

Length

2023-03-10T02:48:40.444940image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-10T02:48:40.492947image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 295865
82.0%
0.0 57204
 
15.9%
2.0 7662
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 417935
38.6%
. 360731
33.3%
1 295865
27.3%
2 7662
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 721462
66.7%
Other Punctuation 360731
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 417935
57.9%
1 295865
41.0%
2 7662
 
1.1%
Other Punctuation
ValueCountFrequency (%)
. 360731
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1082193
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 417935
38.6%
. 360731
33.3%
1 295865
27.3%
2 7662
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1082193
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 417935
38.6%
. 360731
33.3%
1 295865
27.3%
2 7662
 
0.7%

ASA_ESTADO_DDA
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing246733
Missing (%)40.6%
Memory size4.6 MiB
1.0
360731 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1082193
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 360731
59.4%
(Missing) 246733
40.6%

Length

2023-03-10T02:48:40.533156image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-10T02:48:40.578100image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 360731
100.0%

Most occurring characters

ValueCountFrequency (%)
1 360731
33.3%
. 360731
33.3%
0 360731
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 721462
66.7%
Other Punctuation 360731
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 360731
50.0%
0 360731
50.0%
Other Punctuation
ValueCountFrequency (%)
. 360731
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1082193
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 360731
33.3%
. 360731
33.3%
0 360731
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1082193
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 360731
33.3%
. 360731
33.3%
0 360731
33.3%

cod_final
Real number (ℝ)

Distinct405533
Distinct (%)66.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5960775 × 109
Minimum1.0000009 × 109
Maximum9.9996309 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2023-03-10T02:48:40.622504image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1.0000009 × 109
5-th percentile1.2370511 × 109
Q11.9728409 × 109
median2.331671 × 109
Q32.5759237 × 109
95-th percentile6.6029098 × 109
Maximum9.9996309 × 109
Range8.99963 × 109
Interquartile range (IQR)6.0308272 × 108

Descriptive statistics

Standard deviation1.5384556 × 109
Coefficient of variation (CV)0.59260773
Kurtosis9.4313501
Mean2.5960775 × 109
Median Absolute Deviation (MAD)2.8043026 × 108
Skewness3.0467014
Sum1.5770236 × 1015
Variance2.3668455 × 1018
MonotonicityNot monotonic
2023-03-10T02:48:40.678503image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2219251783 15
 
< 0.1%
2435070910 12
 
< 0.1%
2205961756 12
 
< 0.1%
2167071756 12
 
< 0.1%
2165461792 12
 
< 0.1%
6022210156 12
 
< 0.1%
2436280901 12
 
< 0.1%
2208941747 11
 
< 0.1%
2400200992 11
 
< 0.1%
2234911701 11
 
< 0.1%
Other values (405523) 607344
> 99.9%
ValueCountFrequency (%)
1000000901 1
< 0.1%
1000011238 2
< 0.1%
1000011729 1
< 0.1%
1000030892 1
< 0.1%
1000050374 1
< 0.1%
1000051774 1
< 0.1%
1000070956 1
< 0.1%
1000080147 1
< 0.1%
1000090929 2
< 0.1%
1000100829 1
< 0.1%
ValueCountFrequency (%)
9999630929 2
< 0.1%
9999501765 1
< 0.1%
9999360901 1
< 0.1%
9999351383 1
< 0.1%
9999311210 2
< 0.1%
9999300983 1
< 0.1%
9999291383 2
< 0.1%
9999272338 1
< 0.1%
9999200938 2
< 0.1%
9999130156 1
< 0.1%

archivo
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.6 MiB
asigna-acepta_per19.csv
134696 
asigna-acepta_per22.csv
133235 
asigna-acepta_per20.csv
122520 
asigna-acepta_per18.csv
112037 
asigna-acepta_per21.csv
104976 

Length

Max length23
Median length23
Mean length23
Min length23

Characters and Unicode

Total characters13971672
Distinct characters19
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowasigna-acepta_per22.csv
2nd rowasigna-acepta_per22.csv
3rd rowasigna-acepta_per22.csv
4th rowasigna-acepta_per22.csv
5th rowasigna-acepta_per22.csv

Common Values

ValueCountFrequency (%)
asigna-acepta_per19.csv 134696
22.2%
asigna-acepta_per22.csv 133235
21.9%
asigna-acepta_per20.csv 122520
20.2%
asigna-acepta_per18.csv 112037
18.4%
asigna-acepta_per21.csv 104976
17.3%

Length

2023-03-10T02:48:40.727762image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-10T02:48:40.778507image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
asigna-acepta_per19.csv 134696
22.2%
asigna-acepta_per22.csv 133235
21.9%
asigna-acepta_per20.csv 122520
20.2%
asigna-acepta_per18.csv 112037
18.4%
asigna-acepta_per21.csv 104976
17.3%

Most occurring characters

ValueCountFrequency (%)
a 2429856
17.4%
s 1214928
 
8.7%
c 1214928
 
8.7%
e 1214928
 
8.7%
p 1214928
 
8.7%
v 607464
 
4.3%
. 607464
 
4.3%
r 607464
 
4.3%
_ 607464
 
4.3%
t 607464
 
4.3%
Other values (9) 3644784
26.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 10934352
78.3%
Decimal Number 1214928
 
8.7%
Other Punctuation 607464
 
4.3%
Connector Punctuation 607464
 
4.3%
Dash Punctuation 607464
 
4.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2429856
22.2%
s 1214928
11.1%
c 1214928
11.1%
e 1214928
11.1%
p 1214928
11.1%
v 607464
 
5.6%
r 607464
 
5.6%
t 607464
 
5.6%
n 607464
 
5.6%
g 607464
 
5.6%
Decimal Number
ValueCountFrequency (%)
2 493966
40.7%
1 351709
28.9%
9 134696
 
11.1%
0 122520
 
10.1%
8 112037
 
9.2%
Other Punctuation
ValueCountFrequency (%)
. 607464
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 607464
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 607464
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10934352
78.3%
Common 3037320
 
21.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 2429856
22.2%
s 1214928
11.1%
c 1214928
11.1%
e 1214928
11.1%
p 1214928
11.1%
v 607464
 
5.6%
r 607464
 
5.6%
t 607464
 
5.6%
n 607464
 
5.6%
g 607464
 
5.6%
Common
ValueCountFrequency (%)
. 607464
20.0%
_ 607464
20.0%
- 607464
20.0%
2 493966
16.3%
1 351709
11.6%
9 134696
 
4.4%
0 122520
 
4.0%
8 112037
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13971672
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 2429856
17.4%
s 1214928
 
8.7%
c 1214928
 
8.7%
e 1214928
 
8.7%
p 1214928
 
8.7%
v 607464
 
4.3%
. 607464
 
4.3%
r 607464
 
4.3%
_ 607464
 
4.3%
t 607464
 
4.3%
Other values (9) 3644784
26.1%

Interactions

2023-03-10T02:48:21.804814image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:47:53.560089image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:47:55.156782image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:47:56.513777image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:47:58.018432image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:47:59.609900image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:01.020196image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:02.398233image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:03.844485image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:05.179269image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:06.673790image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:08.245197image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:09.678411image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:11.155884image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:12.742214image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:14.175339image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:15.630904image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:17.234649image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2023-03-10T02:48:17.977857image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:19.451306image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:21.048709image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:22.596002image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:47:54.384703image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:47:55.882431image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:47:57.327119image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:47:58.824071image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:00.373474image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:01.777151image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:03.122115image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:04.565555image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:05.988471image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:07.474058image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:09.017355image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:10.471029image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:12.071184image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:13.520117image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:14.976754image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:16.544820image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:18.053464image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:19.522460image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:21.118035image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:22.671523image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:47:54.459889image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:47:55.956368image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:47:57.406866image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:47:58.905791image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:00.448407image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:01.850047image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:03.195977image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:04.634461image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:06.069014image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:07.549676image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:09.094619image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:10.549286image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:12.146450image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:13.595709image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:15.054346image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:16.624313image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:18.130748image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:19.601790image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:21.200145image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:22.746592image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:47:54.639290image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:47:56.029076image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:47:57.481093image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:47:59.091197image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:00.518968image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:01.921251image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:03.264528image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:04.702634image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:06.144769image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:07.728997image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:09.169217image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:10.628656image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:12.218634image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:13.668933image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:15.128737image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:16.702139image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:18.208271image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:19.677235image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:21.277970image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:22.816494image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:47:54.710873image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:47:56.098008image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:47:57.555156image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:47:59.161530image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:00.589111image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:01.986277image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:03.333584image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:04.769933image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:06.218134image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:07.798783image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:09.238659image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:10.703476image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:12.291015image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:13.737748image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:15.195783image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:16.776897image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:18.282153image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:19.749888image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:21.353126image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:22.889314image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:47:54.781280image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:47:56.166259image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:47:57.626823image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:47:59.233132image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:00.659958image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:02.054114image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:03.506103image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:04.835626image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:06.290700image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:07.869082image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:09.309901image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:10.778118image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:12.366672image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:13.807934image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:15.263061image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:16.846788image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:18.354476image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:19.825024image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:21.421975image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:22.964622image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:47:54.858097image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:47:56.233650image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:47:57.702526image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:47:59.311383image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:00.733987image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:02.120678image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:03.573862image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:04.905546image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:06.368356image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:07.942608image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:09.381287image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:10.857071image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:12.442109image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:13.882544image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:15.337034image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:16.921086image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:18.426968image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:19.901752image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:21.499166image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:23.037258image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:47:54.927535image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:47:56.304027image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:47:57.774282image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:47:59.381170image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:00.804744image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:02.190979image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:03.636654image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:04.968522image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:06.440173image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:08.014639image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:09.453630image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:10.928389image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:12.509696image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:13.952575image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:15.407462image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:16.994928image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:18.497974image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:19.974671image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:21.570138image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:23.112482image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:47:55.003682image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:47:56.372416image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:47:57.852882image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:47:59.459595image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:00.878109image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:02.259907image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:03.704937image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:05.036592image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:06.517820image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:08.091607image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:09.529776image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:11.006054image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:12.586837image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:14.028537image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:15.480158image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:17.074631image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:18.571963image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:20.047027image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:21.646919image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:23.194700image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:47:55.086366image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:47:56.443831image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:47:57.931894image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:47:59.538187image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:00.950475image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:02.332169image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:03.776750image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:05.105652image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:06.596799image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:08.168825image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:09.606489image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:11.082408image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:12.666883image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:14.101270image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:15.557289image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:17.154525image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:18.648142image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:20.121561image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:48:21.725324image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-03-10T02:48:40.862375image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Unnamed: 0ASA_IDINI_IDINS_IDCOD_PARROQUIA_RESIDECOD_CANTON_RESIDECOD_PROV_RESIDEPRD_IDPOS_IDPOS_NOTAIES_IDCAM_IDCCP_IDCUS_ID_PADRECUS_ID_HIJOCAR_IDOFA_IDPRD_ID_NUM_POSTULACIONPRD_ID_NUM_ASIGNACIONcod_finalGENEROUSU_NACIONALIDADETNIAPROVINCIA_RESIDESEGMENTACION_PERSONAPOS_PRIORIDADSEGMETO_CARRERAIES_SIGLAS_INSTITTIPO_INSTITUCIONTIPO_FINANCIAMIENTOCAMPUS_CIUDADPROVINCIACANTONAREASUBAREAMODALIDADNIVELJORNADAACEPTA_CUPOASA_ESTADOPER_IDSEGMENTOTIPO_CUPODISCAPACIDADINSTANCIA_POSTULACIONINSTANCIA_ASIGNACIONASA_GRATUIDADGRATUIDADACEPTA_CUPO_DDAarchivo
Unnamed: 01.0000.0670.0620.0620.8060.8080.8160.0900.0770.023-0.0190.1090.0640.0920.0900.0180.0740.0260.0310.0170.0350.0220.1000.5290.0600.0220.0510.3630.0730.0830.3890.3790.3870.0620.0740.0670.0610.1390.0390.0080.1750.0770.1560.0110.0510.0530.0410.0340.0380.140
ASA_ID0.0671.0000.8910.891-0.002-0.003-0.0020.2050.9500.0940.0500.0160.8830.9500.9690.0640.9310.2200.2570.2290.0260.0170.0990.0700.1280.2550.1130.1780.1750.1450.1580.0930.1500.0890.1290.0890.1290.0780.1371.0000.9870.1290.1200.0080.3470.3590.1400.1130.1370.987
INI_ID0.0620.8911.0001.000-0.001-0.002-0.0020.1520.9450.2060.070-0.0150.9320.9380.9380.0330.936-0.030-0.0340.3360.0270.0300.2870.1030.1080.0370.3480.1160.0370.5640.1230.0930.1430.0500.0460.0540.2210.0750.0550.0100.9310.3730.5980.0090.3580.3610.1180.0700.0440.931
INS_ID0.0620.8911.0001.000-0.001-0.002-0.0020.1520.9450.2050.070-0.0150.9320.9380.9380.0340.936-0.030-0.0340.3330.0300.0310.2950.1010.0990.0390.3490.1120.0450.5640.1190.0890.1400.0510.0500.0550.2220.0730.0550.0100.9370.3700.5980.0090.3590.3620.1290.0950.0440.937
COD_PARROQUIA_RESIDE0.806-0.002-0.001-0.0011.0000.9960.9890.0760.013-0.003-0.0240.127-0.0010.0260.0240.0050.0090.0230.0280.0280.0260.0070.0821.0000.0670.0250.0280.4700.0270.0240.5310.5210.5280.0770.0980.0510.0220.0630.0230.0030.0260.0700.0320.0040.0270.0310.0130.0110.0180.026
COD_CANTON_RESIDE0.808-0.003-0.002-0.0020.9961.0000.9930.0710.012-0.003-0.0240.127-0.0030.0250.0230.0060.0080.0240.0280.0270.0260.0070.0821.0000.0670.0250.0280.4700.0270.0240.5310.5210.5280.0770.0980.0510.0220.0630.0230.0030.0260.0700.0320.0040.0270.0310.0130.0110.0180.026
COD_PROV_RESIDE0.816-0.002-0.002-0.0020.9890.9931.0000.0590.013-0.002-0.0260.128-0.0040.0260.0240.0090.0090.0260.0310.0260.0260.0070.0821.0000.0670.0250.0280.4700.0270.0240.5310.5210.5280.0770.0980.0510.0220.0630.0230.0030.0260.0700.0320.0040.0270.0310.0130.0110.0180.026
PRD_ID0.0900.2050.1520.1520.0760.0710.0591.0000.169-0.0020.0650.0680.1660.1650.1620.0270.1600.0020.0020.0490.0850.0330.2280.1391.0000.0490.7610.1980.0480.0890.2230.1430.2220.1400.4810.2140.0680.0820.0390.0000.1540.9990.0980.0970.7550.7550.0380.0360.0360.154
POS_ID0.0770.9500.9450.9450.0130.0120.0130.1691.0000.1690.0760.0120.9350.9710.9720.0570.9380.1450.1230.3520.0300.0210.2780.0720.1130.0720.3610.1520.0990.5690.1410.0800.1660.0700.0870.0880.2310.0910.0990.0120.9250.3770.5980.0100.5370.5430.1240.1090.1080.925
POS_NOTA0.0230.0940.2060.205-0.003-0.003-0.002-0.0020.1691.000-0.2380.0270.2650.2090.195-0.1600.233-0.215-0.2430.1900.0260.0140.1090.1030.0930.0800.1040.1980.2570.1820.1660.1290.1700.1410.1730.0810.1510.1630.0780.0050.2010.1420.2070.0020.1320.1390.0540.0360.0780.201
IES_ID-0.0190.0500.0700.070-0.024-0.024-0.0260.0650.076-0.2381.0000.316-0.0180.0060.0260.3120.0540.0700.079-0.0120.1010.0080.0440.2070.0750.0430.1951.0000.9520.4210.6230.3650.5520.2060.2540.1660.4620.2400.0610.0000.0220.0790.5180.0210.1270.1310.1100.1090.0540.022
CAM_ID0.1090.016-0.015-0.0150.1270.1270.1280.0680.0120.0270.3161.0000.016-0.038-0.0220.210-0.0070.0880.0860.0270.1180.0090.0590.3750.1020.0470.1200.8650.8540.2220.7040.5520.6690.1920.2370.2260.4160.3830.0650.0040.0810.1050.5330.0150.1220.1250.0610.0540.0590.081
CCP_ID0.0640.8830.9320.932-0.001-0.003-0.0040.1660.9350.265-0.0180.0161.0000.9400.939-0.0160.937-0.041-0.0440.3740.0460.0220.2820.1620.1040.0370.3540.4070.2980.5710.3410.2340.3500.0940.1080.1140.2870.2040.0460.0100.9320.3720.6210.0070.3640.3670.1120.0780.0320.932
CUS_ID_PADRE0.0920.9500.9380.9380.0260.0250.0260.1650.9710.2090.006-0.0380.9401.0000.9920.0260.9380.1450.1200.3550.0380.0200.2780.1080.1840.0880.3920.2390.2310.5700.2080.1510.2270.0870.1480.1430.2570.1270.0800.0120.9190.4260.6340.0090.6340.6390.1240.1090.0680.919
CUS_ID_HIJO0.0900.9690.9380.9380.0240.0230.0240.1620.9720.1950.026-0.0220.9390.9921.0000.0370.9380.1450.1700.3540.0380.0200.2780.1000.1840.1110.3920.2240.2120.5690.1960.1380.2160.0850.1480.1390.2520.1200.0930.0120.9160.4270.6280.0080.6370.6520.1250.1100.0870.916
CAR_ID0.0180.0640.0330.0340.0050.0060.0090.0270.057-0.1600.3120.210-0.0160.0260.0371.0000.0270.1230.133-0.0140.1600.0060.0300.1060.1450.0320.1530.2770.6630.1370.3050.1720.2850.3750.5190.1480.3700.1970.0500.0020.0330.1460.3660.0100.1590.1610.0430.0390.0500.033
OFA_ID0.0740.9310.9360.9360.0090.0080.0090.1600.9380.2330.054-0.0070.9370.9380.9380.0271.000-0.036-0.0370.3600.0270.0260.3520.0880.1180.0260.4430.1210.0580.5640.1510.0950.1810.0710.0810.0510.2260.0490.0420.0100.8620.4650.6000.0080.4540.4580.1190.0760.0240.862
PRD_ID_NUM_POSTULACION0.0260.220-0.030-0.0300.0230.0240.0260.0020.145-0.2150.0700.088-0.0410.1450.1450.123-0.0361.0000.871-0.0480.0460.0220.0540.0300.6270.0870.7060.1660.1310.1270.2540.0910.2540.0820.2800.1470.0920.0680.0730.0000.1510.6300.1160.0041.0001.0000.0480.0960.0720.151
PRD_ID_NUM_ASIGNACION0.0310.257-0.034-0.0340.0280.0280.0310.0020.123-0.2430.0790.086-0.0440.1200.1700.133-0.0370.8711.000-0.0500.0460.0230.0510.0330.5910.2290.6020.1730.1400.1370.2570.0910.2570.0860.2720.1470.0930.0700.1490.0000.1490.5950.1150.0040.9501.0000.0500.0970.1500.149
cod_final0.0170.2290.3360.3330.0280.0270.0260.0490.3520.190-0.0120.0270.3740.3550.354-0.0140.360-0.048-0.0501.0000.0210.0460.0560.0480.0320.0080.0460.0690.0470.0740.0610.0530.0610.0420.0500.0780.0340.0990.0180.0000.0800.0520.0790.0030.0480.0490.1270.1120.0180.062
GENERO0.0350.0260.0270.0300.0260.0260.0260.0850.0300.0260.1010.1180.0460.0380.0380.1600.0270.0460.0460.0211.0000.0070.0370.0570.0910.0210.0840.1540.0930.0230.1430.0820.1350.3230.3510.1050.1120.1170.0170.0000.0250.0930.0750.0200.0860.0860.0190.0180.0180.025
USU_NACIONALIDAD0.0220.0170.0300.0310.0070.0070.0070.0330.0210.0140.0080.0090.0220.0200.0200.0060.0260.0220.0230.0460.0071.0000.0490.0110.0180.0000.0180.0060.0130.0260.0010.0100.0000.0070.0180.0100.0100.0130.0000.0000.0350.0100.0280.0000.0290.0270.0380.0400.0060.035
ETNIA0.1000.0990.2870.2950.0820.0820.0820.2280.2780.1090.0440.0590.2820.2780.2780.0300.3520.0540.0510.0560.0370.0491.0000.1400.1320.0320.2020.1140.0590.4320.1270.1170.1320.0380.0360.0440.1760.0810.0480.0070.4560.2280.4590.0080.1800.1830.0630.0620.0300.456
PROVINCIA_RESIDE0.5290.0700.1030.1011.0001.0001.0000.1390.0720.1030.2070.3750.1620.1080.1000.1060.0880.0300.0330.0480.0570.0110.1401.0000.0850.0480.0600.4880.1670.1280.5650.5730.5630.0850.0900.1440.1090.2650.0660.0050.1100.0710.2780.0190.0450.0450.0420.0390.0550.110
SEGMENTACION_PERSONA0.0600.1280.1080.0990.0670.0670.0671.0000.1130.0930.0750.1020.1040.1840.1840.1450.1180.6270.5910.0320.0910.0180.1320.0851.0000.0520.8690.1290.1120.0920.2370.0980.2180.0940.3220.2230.1240.0880.0410.0000.1591.0000.1110.0970.8670.8670.0380.0370.0370.159
POS_PRIORIDAD0.0220.2550.0370.0390.0250.0250.0250.0490.0720.0800.0430.0470.0370.0880.1110.0320.0260.0870.2290.0080.0210.0000.0320.0480.0521.0000.0600.0930.0620.0600.0780.0470.0770.0580.0810.0300.0330.0300.2260.0000.0360.0790.0390.0070.1450.4780.0420.0410.2260.036
SEGMETO_CARRERA0.0510.1130.3480.3490.0280.0280.0280.7610.3610.1040.1950.1200.3540.3920.3920.1530.4430.7060.6020.0460.0840.0180.2020.0600.8690.0601.0000.2780.1580.7420.2350.0990.2240.0950.2980.2320.2420.1040.0940.0090.5690.8660.6410.0430.9610.9630.0790.0780.0790.569
IES_SIGLAS_INSTIT0.3630.1780.1160.1120.4700.4700.4700.1980.1520.1981.0000.8650.4070.2390.2240.2770.1210.1660.1730.0690.1540.0060.1140.4880.1290.0930.2781.0001.0000.8200.6660.8230.6880.2610.2360.4560.5930.6340.1130.0100.1550.0680.6580.0390.1370.1290.2100.2060.1050.155
TIPO_INSTITUCION0.0730.1750.0370.0450.0270.0270.0270.0480.0990.2570.9520.8540.2980.2310.2120.6630.0580.1310.1400.0470.0930.0130.0590.1670.1120.0620.1581.0001.0000.2850.5750.2840.5100.3720.4820.3140.9610.4490.0740.0000.0160.1170.7510.0100.2120.2200.0840.0780.0630.016
TIPO_FINANCIAMIENTO0.0830.1450.5640.5640.0240.0240.0240.0890.5690.1820.4210.2220.5710.5700.5690.1370.5640.1270.1370.0740.0230.0260.4320.1280.0920.0600.7420.8200.2851.0000.2930.1720.2760.1780.1650.1380.2930.1210.0850.0100.5640.5700.6410.0370.5680.5800.2560.2550.0750.564
CAMPUS_CIUDAD0.3890.1580.1230.1190.5310.5310.5310.2230.1410.1660.6230.7040.3410.2080.1960.3050.1510.2540.2570.0610.1430.0010.1270.5650.2370.0780.2350.6660.5750.2931.0000.9800.9820.2440.2700.4510.3490.5740.0860.0000.1640.1310.5100.0240.1900.1780.0990.0900.0770.164
PROVINCIA0.3790.0930.0930.0890.5210.5210.5210.1430.0800.1290.3650.5520.2340.1510.1380.1720.0950.0910.0910.0530.0820.0100.1170.5730.0980.0470.0990.8230.2840.1720.9801.0000.9800.1520.1640.2750.1720.4000.0580.0070.1070.0780.4040.0160.0780.0790.0640.0580.0480.107
CANTON0.3870.1500.1430.1400.5280.5280.5280.2220.1660.1700.5520.6690.3500.2270.2160.2850.1810.2540.2570.0610.1350.0000.1320.5630.2180.0770.2240.6880.5100.2760.9820.9801.0000.2290.2380.4350.2810.5590.0830.0000.2030.1260.5270.0230.1840.1740.0860.0760.0740.203
AREA0.0620.0890.0500.0510.0770.0770.0770.1400.0700.1410.2060.1920.0940.0870.0850.3750.0710.0820.0860.0420.3230.0070.0380.0850.0940.0580.0950.2610.3720.1780.2440.1520.2291.0000.8710.2650.2040.2830.0710.0030.0760.0870.2290.0230.0950.0980.0900.0800.0700.076
SUBAREA0.0740.1290.0460.0500.0980.0980.0980.4810.0870.1730.2540.2370.1080.1480.1480.5190.0810.2800.2720.0500.3510.0180.0360.0900.3220.0810.2980.2360.4820.1650.2700.1640.2380.8711.0000.3830.2870.3030.0830.0030.0690.2080.2810.0230.2370.2190.0980.0850.0830.069
MODALIDAD0.0670.0890.0540.0550.0510.0510.0510.2140.0880.0810.1660.2260.1140.1430.1390.1480.0510.1470.1470.0780.1050.0100.0440.1440.2230.0300.2320.4560.3140.1380.4510.2750.4350.2650.3831.0000.1790.5010.0180.0000.0650.2240.2580.0140.2400.2420.0650.0450.0180.065
NIVEL0.0610.1290.2210.2220.0220.0220.0220.0680.2310.1510.4620.4160.2870.2570.2520.3700.2260.0920.0930.0340.1120.0100.1760.1090.1240.0330.2420.5930.9610.2930.3490.1720.2810.2040.2870.1791.0000.2120.0570.0070.2550.2620.5180.0090.2610.2710.0630.0590.0440.255
JORNADA0.1390.0780.0750.0730.0630.0630.0630.0820.0910.1630.2400.3830.2040.1270.1200.1970.0490.0680.0700.0990.1170.0130.0810.2650.0880.0300.1040.6340.4490.1210.5740.4000.5590.2830.3030.5010.2121.0000.0380.0010.0660.0990.2560.0150.1270.1320.0620.0400.0320.066
ACEPTA_CUPO0.0390.1370.0550.0550.0230.0230.0230.0390.0990.0780.0610.0650.0460.0800.0930.0500.0420.0730.1490.0180.0170.0000.0480.0660.0410.2260.0940.1130.0740.0850.0860.0580.0830.0710.0830.0180.0570.0381.0000.0020.0430.2150.0530.0010.1530.2550.0800.0661.0000.043
ASA_ESTADO0.0081.0000.0100.0100.0030.0030.0030.0000.0120.0050.0000.0040.0100.0120.0120.0020.0100.0000.0000.0000.0000.0000.0070.0050.0000.0000.0090.0100.0000.0100.0000.0070.0000.0030.0030.0000.0070.0010.0021.0000.0100.0070.0090.0000.0110.0101.0001.0001.0000.010
PER_ID0.1750.9870.9310.9370.0260.0260.0260.1540.9250.2010.0220.0810.9320.9190.9160.0330.8620.1510.1490.0800.0250.0350.4560.1100.1590.0360.5690.1550.0160.5640.1640.1070.2030.0760.0690.0650.2550.0660.0430.0101.0000.6020.5970.0070.5870.5900.1670.1110.0271.000
SEGMENTO0.0770.1290.3730.3700.0700.0700.0700.9990.3770.1420.0790.1050.3720.4260.4270.1460.4650.6300.5950.0520.0930.0100.2280.0711.0000.0790.8660.0680.1170.5700.1310.0780.1260.0870.2080.2240.2620.0990.2150.0070.6021.0000.6090.0970.6750.6250.0380.0360.0370.602
TIPO_CUPO0.1560.1200.5980.5980.0320.0320.0320.0980.5980.2070.5180.5330.6210.6340.6280.3660.6000.1160.1150.0790.0750.0280.4590.2780.1110.0390.6410.6580.7510.6410.5100.4040.5270.2290.2810.2580.5180.2560.0530.0090.5970.6091.0000.0180.6020.6150.0900.0860.0570.597
DISCAPACIDAD0.0110.0080.0090.0090.0040.0040.0040.0970.0100.0020.0210.0150.0070.0090.0080.0100.0080.0040.0040.0030.0200.0000.0080.0190.0970.0070.0430.0390.0100.0370.0240.0160.0230.0230.0230.0140.0090.0150.0010.0000.0070.0970.0181.0000.0070.0070.0050.0040.0000.007
INSTANCIA_POSTULACION0.0510.3470.3580.3590.0270.0270.0270.7550.5370.1320.1270.1220.3640.6340.6370.1590.4541.0000.9500.0480.0860.0290.1800.0450.8670.1450.9610.1370.2120.5680.1900.0780.1840.0950.2370.2400.2610.1270.1530.0110.5870.6750.6020.0071.0000.9850.1610.1840.1390.587
INSTANCIA_ASIGNACION0.0530.3590.3610.3620.0310.0310.0310.7550.5430.1390.1310.1250.3670.6390.6520.1610.4581.0001.0000.0490.0860.0270.1830.0450.8670.4780.9630.1290.2200.5800.1780.0790.1740.0980.2190.2420.2710.1320.2550.0100.5900.6250.6150.0070.9851.0000.1600.1820.2420.590
ASA_GRATUIDAD0.0410.1400.1180.1290.0130.0130.0130.0380.1240.0540.1100.0610.1120.1240.1250.0430.1190.0480.0500.1270.0190.0380.0630.0420.0380.0420.0790.2100.0840.2560.0990.0640.0860.0900.0980.0650.0630.0620.0801.0000.1670.0380.0900.0050.1610.1601.0000.9990.0800.167
GRATUIDAD0.0340.1130.0700.0950.0110.0110.0110.0360.1090.0360.1090.0540.0780.1090.1100.0390.0760.0960.0970.1120.0180.0400.0620.0390.0370.0410.0780.2060.0780.2550.0900.0580.0760.0800.0850.0450.0590.0400.0661.0000.1110.0360.0860.0040.1840.1820.9991.0000.0660.111
ACEPTA_CUPO_DDA0.0380.1370.0440.0440.0180.0180.0180.0360.1080.0780.0540.0590.0320.0680.0870.0500.0240.0720.1500.0180.0180.0060.0300.0550.0370.2260.0790.1050.0630.0750.0770.0480.0740.0700.0830.0180.0440.0321.0001.0000.0270.0370.0570.0000.1390.2420.0800.0661.0000.027
archivo0.1400.9870.9310.9370.0260.0260.0260.1540.9250.2010.0220.0810.9320.9190.9160.0330.8620.1510.1490.0620.0250.0350.4560.1100.1590.0360.5690.1550.0160.5640.1640.1070.2030.0760.0690.0650.2550.0660.0430.0101.0000.6020.5970.0070.5870.5900.1670.1110.0271.000

Missing values

2023-03-10T02:48:23.719749image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-10T02:48:25.454936image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-03-10T02:48:33.833137image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Unnamed: 0ASA_IDINI_IDINS_IDGENEROUSU_FECHA_NACUSU_NACIONALIDADETNIACOD_PARROQUIA_RESIDEPARROQUIA_RESIDECOD_CANTON_RESIDECANTON_RESIDECOD_PROV_RESIDEPROVINCIA_RESIDEPRD_IDSEGMENTACION_PERSONAPOS_IDPOS_NOTAPOS_PRIORIDADSEGMETO_CARRERAIES_IDIES_SIGLAS_INSTITNOMBRE_INSTITUCIONTIPO_INSTITUCIONTIPO_FINANCIAMIENTOCAMPUS_NOMBRECAM_IDCAMPUS_CIUDADPROVINCIACANTONPARROQUIACCP_IDCUS_ID_PADRECUS_ID_HIJOCAR_IDCARRERAAREASUBAREAMODALIDADNIVELJORNADAACEPTA_CUPOASA_ESTADOOFA_IDASA_FECHA_ACEPTACIONEXONERADOOFA_ESTADOASA_EXONERAPER_IDSEGMENTOTIPO_CUPODISCAPACIDADASA_BECAPRD_ID_NUM_POSTULACIONPRD_ID_NUM_ASIGNACIONINSTANCIA_POSTULACIONINSTANCIA_ASIGNACIONASA_OBSERVACIONASA_GRATUIDADGRATUIDADACEPTA_CUPO_DDAASA_ESTADO_DDAcod_finalarchivo
012242680.06835166.011425152.0FEMENINO23233.0ECUATORIANAMestizo/a170109.0CHIMBACALLE1701.0DISTRITO METROPOLITANO DE QUITO17.0PICHINCHA676.0POLITICA DE ACCION AFIRMATIVA26943201.0830.01.0POLITICA DE ACCION AFIRMATIVA1022.0NaNINSTITUTO TECNOLÓGICO SUPERIOR LIBERTADIAUTOFINANCIADAMATRIZ - Sede QUITO1226.0QUITOPICHINCHADISTRITO METROPOLITANO DE QUITOQUITO DISTRITO METROPOLITANO, CABECERA CANTONAL, CAPITAL PROVINCIAL Y DE LA REPUBLICA DEL ECUADOR31999.0305283.0305283.07498.0TECNOLOGIA SUPERIOR EN PODOLOGIASALUD Y BIENESTARSALUDPRESENCIALTERCER NIVEL TECNOLÓGICO SUPERIORVESPERTINA0.01.0180428.0NaNN1.00.022.0/POLITICA DE ACCION AFIRMATIVAPRIMER SEMESTRENO0.0446.0446.0PRIMERA POSTULACIONPRIMERA ASIGNACIÓN DE CUPOS1era Asignación de cupos 1era Postulación P22 de acuerdo a las reglas remitidas en el RF CGTIC-PROY-2021-RF-0730.0MANTIENE GRATUIDAD0.01.02702700138asigna-acepta_per22.csv
122265703.07096694.011948261.0MASCULINO28082.0ECUATORIANAMestizo/a90110.0ROCAFUERTE901.0GUAYAQUIL9.0GUAYAS47.0POBLACION GENERAL27592391.0760.05.0OFERTA PÚBLICA86.0UTMUNIVERSIDAD TECNICA DE MANABIUPÚBLICAMATRIZ - PORTOVIEJO294.0PORTOVIEJOMANABIPORTOVIEJOPORTOVIEJO29333.0304872.0306255.05457.0TURISMOSERVICIOSSERVICIOS PERSONALESEN LINEATERCER NIVELNO APLICA JORNADA1.01.0175218.044479.457581N1.00.022.0POBLACION GENERALNIVELACIÓNNO0.0446.0463.0PRIMERA POSTULACIONSEGUNDA ASIGNACIÓN DE CUPOS2da Asignación de cupos 1era Postulación p22 de acuerdo a las reglas remitidas en el RF CGTIC-PROY-2021-RF-0754.0NaN1.01.07115950165asigna-acepta_per22.csv
232237198.07479027.012035579.0FEMENINO25700.0ECUATORIANANaNNaNNaNNaNNaNNaNNaN47.0POBLACION GENERAL27608814.0851.01.0OFERTA PÚBLICA59.0UNEMIUNIVERSIDAD ESTATAL DE MILAGROUPÚBLICAMATRIZ - MILAGRO32.0MILAGROGUAYASMILAGROMILAGRO, CABECERA CANTONAL30216.0304825.0304825.05476.0PSICOLOGIACIENCIAS SOCIALES, PERIODISMO, INFORMACION Y DERECHOCIENCIAS SOCIALES Y DEL COMPORTAMIENTOEN LINEATERCER NIVELNO APLICA JORNADA0.01.0180856.0NaNN1.00.022.0POBLACION GENERALNIVELACIÓNNO0.0446.0446.0PRIMERA POSTULACIONPRIMERA ASIGNACIÓN DE CUPOS1era Asignación de cupos 1era Postulación P22 de acuerdo a las reglas remitidas en el RF CGTIC-PROY-2021-RF-0730.0MANTIENE GRATUIDAD0.01.02061520110asigna-acepta_per22.csv
342222225.07068123.011891094.0FEMENINO28625.0ECUATORIANAMestizo/a10109.0MONAY101.0CUENCA1.0AZUAY676.0POLITICA DE ACCION AFIRMATIVA26606666.0866.01.0OFERTA PÚBLICA48.0UCUENCAUNIVERSIDAD DE CUENCAUPÚBLICAMATRIZ - AZUAY.743.0CUENCAAZUAYCUENCACUENCA, CABECERA CANTONAL Y CAPITAL PROVINCIAL.31757.0303576.0303576.05511.0ARTES VISUALESARTES Y HUMANIDADESARTESPRESENCIALTERCER NIVELINTENSIVA1.01.0177884.044475.322303N1.00.022.0/POLITICA DE ACCION AFIRMATIVAPRIMER SEMESTRENO0.0446.0446.0PRIMERA POSTULACIONPRIMERA ASIGNACIÓN DE CUPOS1era Asignación de cupos 1era Postulación P22 de acuerdo a las reglas remitidas en el RF CGTIC-PROY-2021-RF-0730.0MANTIENE GRATUIDAD1.01.02488410110asigna-acepta_per22.csv
452202366.06928614.011612070.0MASCULINO28266.0ECUATORIANAMestizo/a10105.0EL VECINO101.0CUENCA1.0AZUAY676.0POLITICA DE ACCION AFIRMATIVA27135610.0695.01.0OFERTA PÚBLICA497.0NaNINSTITUTO TECNOLÓGICO SUPERIOR LUIS ROGERIO GONZALEZIPÚBLICAMATRIZ - AZOGUES554.0AZOGUESCAÑARAZOGUESAZOGUES30189.0303828.0303828.05113.0TECNOLOGIA SUPERIOR EN ELECTRICIDADINGENIERIA, INDUSTRIA Y CONSTRUCCIONINGENIERIA Y PROFESIONES AFINESPRESENCIALTERCER NIVEL TECNOLÓGICO SUPERIORNOCTURNA1.01.0174977.044474.999468N1.00.022.0/POLITICA DE ACCION AFIRMATIVAPRIMER SEMESTRENO0.0446.0446.0PRIMERA POSTULACIONPRIMERA ASIGNACIÓN DE CUPOS1era Asignación de cupos 1era Postulación P22 de acuerdo a las reglas remitidas en el RF CGTIC-PROY-2021-RF-0730.0MANTIENE GRATUIDAD1.01.02553310129asigna-acepta_per22.csv
562278261.06871138.011497114.0MASCULINO27921.0ECUATORIANAMestizo/a10113.0TOTORACOCHA101.0CUENCA1.0AZUAY676.0POLITICA DE ACCION AFIRMATIVA28868045.0765.01.0OFERTA PÚBLICA59.0UNEMIUNIVERSIDAD ESTATAL DE MILAGROUPÚBLICAMATRIZ - MILAGRO32.0MILAGROGUAYASMILAGROMILAGRO, CABECERA CANTONAL30219.0309363.0309363.04911.0TECNOLOGIAS DE LA INFORMACIONTECNOLOGIAS DE LA INFORMACION Y LA COMUNICACION (TIC)TECNOLOGIAS DE LA INFORMACION Y LA COMUNICACION (TIC)EN LINEATERCER NIVELNO APLICA JORNADA0.01.0172714.0NaNN1.00.022.0/POLITICA DE ACCION AFIRMATIVANIVELACIÓNNO0.0492.0492.0SEGUNDA POSTULACIÓNTERCERA ASIGNACIÓN DE CUPOS3ra Asignación de cupos 2da Postulación P22 de acuerdo a las reglas remitidas en el CGTIC-PROY-2022-RF-0810.0MANTIENE GRATUIDAD0.01.02705290101asigna-acepta_per22.csv
672191225.06831573.011417986.0FEMENINO29060.0ECUATORIANAMestizo/a110102.0SAN SEBASTIÁN1101.0LOJA11.0LOJA676.0POLITICA DE ACCION AFIRMATIVA26713308.0898.01.0OFERTA PÚBLICA72.0UNLUNIVERSIDAD NACIONAL DE LOJAUPÚBLICAMATRIZ - LOJA275.0LOJALOJALOJALOJA, CABECERA CANTONAL Y CAPITAL PROVINCIAL29750.0303896.0303896.04781.0DERECHOCIENCIAS SOCIALES, PERIODISMO, INFORMACION Y DERECHODERECHOPRESENCIALTERCER NIVELNOCTURNA1.01.0176573.044474.579132N1.00.022.0/POLITICA DE ACCION AFIRMATIVAPRIMER SEMESTRENO0.0446.0446.0PRIMERA POSTULACIONPRIMERA ASIGNACIÓN DE CUPOS1era Asignación de cupos 1era Postulación P22 de acuerdo a las reglas remitidas en el RF CGTIC-PROY-2021-RF-0730.0MANTIENE GRATUIDAD1.01.02714170192asigna-acepta_per22.csv
782237549.06880661.011516168.0MASCULINO26354.0ECUATORIANAMestizo/a10111.0SAN SEBASTIÁN101.0CUENCA1.0AZUAY676.0POLITICA DE ACCION AFIRMATIVA27892679.0789.01.0OFERTA PÚBLICA59.0UNEMIUNIVERSIDAD ESTATAL DE MILAGROUPÚBLICAMATRIZ - MILAGRO32.0MILAGROGUAYASMILAGROMILAGRO, CABECERA CANTONAL30216.0304825.0304825.05476.0PSICOLOGIACIENCIAS SOCIALES, PERIODISMO, INFORMACION Y DERECHOCIENCIAS SOCIALES Y DEL COMPORTAMIENTOEN LINEATERCER NIVELNO APLICA JORNADA1.01.0180856.044474.816655N1.00.022.0/POLITICA DE ACCION AFIRMATIVANIVELACIÓNNO0.0446.0446.0PRIMERA POSTULACIONPRIMERA ASIGNACIÓN DE CUPOS1era Asignación de cupos 1era Postulación P22 de acuerdo a las reglas remitidas en el RF CGTIC-PROY-2021-RF-0730.0MANTIENE GRATUIDAD1.01.02694260192asigna-acepta_per22.csv
892299482.07004881.011764645.0FEMENINO26424.0ECUATORIANAMestizo/a10113.0TOTORACOCHA101.0CUENCA1.0AZUAY676.0POLITICA DE ACCION AFIRMATIVA29578514.0774.01.0POLITICA DE ACCION AFIRMATIVA456.0NaNINSTITUTO TECNOLÓGICO SUPERIOR AMERICAN COLLEGEIAUTOFINANCIADASEDE - CUENCA1453.0CUENCAAZUAYCUENCACUENCA, CABECERA CANTONAL Y CAPITAL PROVINCIAL.29712.0313474.0313474.04676.0GERONTOLOGIASALUD Y SERVICIOS SOCIALESSERVICIOS SOCIALESPRESENCIALTERCER NIVEL TÉCNICO SUPERIORNOCTURNA1.01.0177291.044511.47169N1.00.022.0/POLITICA DE ACCION AFIRMATIVAPRIMER SEMESTRENO0.01092.01092.0CUARTA POSTULACIÓNSEXTA ASIGNACIÓN DE CUPOS6ta Asignación de cupos 4ta Postulación P22 de acuerdo a las reglas remitidas en el RF CGTIC-PROY-2022-RF-0930.0MANTIENE GRATUIDAD1.01.02762040147asigna-acepta_per22.csv
9102282488.06856446.011467754.0MASCULINO29812.0ECUATORIANANegro/a90114.0XIMENA901.0GUAYAQUIL9.0GUAYAS676.0POLITICA DE ACCION AFIRMATIVA28558265.0716.02.0OFERTA PÚBLICA59.0UNEMIUNIVERSIDAD ESTATAL DE MILAGROUPÚBLICAMATRIZ - MILAGRO32.0MILAGROGUAYASMILAGROMILAGRO, CABECERA CANTONAL30222.0310197.0310197.05457.0TURISMOSERVICIOSSERVICIOS PERSONALESEN LINEATERCER NIVELNO APLICA JORNADA0.01.0174128.0NaNN1.00.022.0/POLITICA DE ACCION AFIRMATIVANIVELACIÓNNO0.0492.0492.0SEGUNDA POSTULACIÓNTERCERA ASIGNACIÓN DE CUPOS3ra Asignación de cupos 2da Postulación P22 de acuerdo a las reglas remitidas en el CGTIC-PROY-2022-RF-0810.0MANTIENE GRATUIDAD0.01.02705320192asigna-acepta_per22.csv
Unnamed: 0ASA_IDINI_IDINS_IDGENEROUSU_FECHA_NACUSU_NACIONALIDADETNIACOD_PARROQUIA_RESIDEPARROQUIA_RESIDECOD_CANTON_RESIDECANTON_RESIDECOD_PROV_RESIDEPROVINCIA_RESIDEPRD_IDSEGMENTACION_PERSONAPOS_IDPOS_NOTAPOS_PRIORIDADSEGMETO_CARRERAIES_IDIES_SIGLAS_INSTITNOMBRE_INSTITUCIONTIPO_INSTITUCIONTIPO_FINANCIAMIENTOCAMPUS_NOMBRECAM_IDCAMPUS_CIUDADPROVINCIACANTONPARROQUIACCP_IDCUS_ID_PADRECUS_ID_HIJOCAR_IDCARRERAAREASUBAREAMODALIDADNIVELJORNADAACEPTA_CUPOASA_ESTADOOFA_IDASA_FECHA_ACEPTACIONEXONERADOOFA_ESTADOASA_EXONERAPER_IDSEGMENTOTIPO_CUPODISCAPACIDADASA_BECAPRD_ID_NUM_POSTULACIONPRD_ID_NUM_ASIGNACIONINSTANCIA_POSTULACIONINSTANCIA_ASIGNACIONASA_OBSERVACIONASA_GRATUIDADGRATUIDADACEPTA_CUPO_DDAASA_ESTADO_DDAcod_finalarchivo
607454112028NaN4175729.07475792.0MASCULINO37325BRASILAfrodescendiente30102.0AZOGUES301.0AZOGUES3.0CAÑAR676.0POLITICA DE ACCION AFIRMATIVA18404831.0678.01.0POLITICA DE ACCION AFIRMATIVA79.0NaNUNIVERSIDAD POLITECNICA SALESIANAUCOFINANCIADAGUAYAQUIL459.0GUAYAQUILGUAYASGUAYAQUILGUAYAQUIL, CABECERA CANTONAL Y CAPITAL PROVINCIAL21207.0276133.0276133.04749.0COMPUTACIONTECNOLOGIAS DE LA INFORMACION Y LA COMUNICACION (TIC)TECNOLOGIAS DE LA INFORMACION Y LA COMUNICACION (TIC)PRESENCIALTERCER NIVELMATUTINA1.01.0102891.043726,40104N1.00.018.0POLITICA DE ACCION AFIRMATIVAPRIMER SEMESTRENO0.0494.0494.0TERCERA POSTULACIONQUINTA ASIGNACIÓN DE CUPOS5ta Asignación de cupos 3ra Postulación P18 de acuerdo a las reglas remitidas en el RF CGTIC-PROY-2019-RF-119 y memorando Nro. SENESCYT-SAES-DDA-2019-0521-M del 18-09-2019NaNNaNNaNNaN2275041710asigna-acepta_per18.csv
607455112029NaN4139392.07403100.0MASCULINO36894BRASILBlanco180101.0ATOCHA – FICOA1801.0AMBATO18.0TUNGURAHUA47.0POBLACION GENERAL17361196.0845.02.0OFERTA PÚBLICA82.0UTAUNIVERSIDAD TECNICA DE AMBATOUPÚBLICAMATRIZ - AMBATO288.0AMBATOTUNGURAHUAAMBATOLA MERCED19556.0268183.0268183.05039.0MECANICAINGENIERIA, INDUSTRIA Y CONSTRUCCIONINGENIERIA Y PROFESIONES AFINESPRESENCIALTERCER NIVELINTENSIVA1.01.099858.043692,88163N1.00.018.0/POBLACION GENERALNIVELACIÓNNO0.0446.0446.0PRIMERA POSTULACIONPRIMERA ASIGNACIÓN DE CUPOS1era Asignación de cupos 1era Postulación P18 de acuerdo a las reglas remitidas en el RF CGTIC-PROY-2019-RF-104 y memorando Nro.SENESCYT-SAES-DDA-2019-0456-M del 14 septiembre 2019NaNNaNNaNNaN1851962274asigna-acepta_per18.csv
607456112030NaN4164347.07453013.0MASCULINO34914CUBABlanco90601.0DAULE906.0DAULE9.0GUAYAS47.0POBLACION GENERAL17670405.0820.02.0OFERTA PÚBLICA51.0UGUNIVERSIDAD DE GUAYAQUILUPÚBLICAMATRIZ - GUAYAQUIL29.0GUAYAQUILGUAYASGUAYAQUILGUAYAQUIL, CABECERA CANTONAL Y CAPITAL PROVINCIAL19387.0272443.0272443.05167.0DISEÑO GRAFICOARTES Y HUMANIDADESARTESPRESENCIALTERCER NIVELMATUTINA1.01.095930.043712,35222N1.00.018.0/POBLACION GENERALNIVELACIÓNNO0.0492.0492.0SEGUNDA POSTULACIÓNTERCERA ASIGNACIÓN DE CUPOS2da Asignación de cupos 2da Postulación P18 de acuerdo a las reglas remitidas en el RF CGTIC-PROY-2019-RF-110 y memorando Nro. SENESCYT-SAES-DDA-2019-0492-M del 03/09/2019NaNNaNNaNNaN9286454001asigna-acepta_per18.csv
607457112031NaN4128821.07381991.0FEMENINO36385CUBABlanco100302.0SAN FRANCISCO1003.0COTACACHI10.0IMBABURA47.0POBLACION GENERAL18397791.0976.01.0OFERTA PÚBLICA46.0UCEUNIVERSIDAD CENTRAL DEL ECUADORUPÚBLICAMATRIZ - QUITO1317.0QUITOPICHINCHADISTRITO METROPOLITANO DE QUITOQUITO DISTRITO METROPOLITANO, CABECERA CANTONAL, CAPITAL PROVINCIAL Y DE LA REPUBLICA DEL ECUADOR20685.0274981.0274981.04913.0ODONTOLOGIASALUD Y BIENESTARSALUDPRESENCIALTERCER NIVELINTENSIVA1.01.098317.043726,40104N1.00.018.0/POBLACION GENERALNIVELACIÓNNO0.0494.0494.0TERCERA POSTULACIONQUINTA ASIGNACIÓN DE CUPOS5ta Asignación de cupos 3ra Postulación P18 de acuerdo a las reglas remitidas en el RF CGTIC-PROY-2019-RF-119 y memorando Nro. SENESCYT-SAES-DDA-2019-0521-M del 18-09-2019NaNNaNNaNNaN2064586283asigna-acepta_per18.csv
607458112032NaN4015555.07155418.0MASCULINO36733CUBABlanco170177.0POMASQUI1701.0DISTRITO METROPOLITANO DE QUITO17.0PICHINCHA676.0POLITICA DE ACCION AFIRMATIVA17201207.0930.02.0OFERTA PÚBLICA46.0UCEUNIVERSIDAD CENTRAL DEL ECUADORUPÚBLICAMATRIZ - QUITO1317.0QUITOPICHINCHADISTRITO METROPOLITANO DE QUITOQUITO DISTRITO METROPOLITANO, CABECERA CANTONAL, CAPITAL PROVINCIAL Y DE LA REPUBLICA DEL ECUADOR20662.0268258.0268258.04781.0DERECHOCIENCIAS SOCIALES, PERIODISMO, INFORMACION Y DERECHODERECHOPRESENCIALTERCER NIVELINTENSIVA1.01.096914.043692,83652N1.00.018.0POLITICA DE ACCION AFIRMATIVANIVELACIÓNNO0.0446.0446.0PRIMERA POSTULACIONPRIMERA ASIGNACIÓN DE CUPOS1era Asignación de cupos 1era Postulación P18 de acuerdo a las reglas remitidas en el RF CGTIC-PROY-2019-RF-104 y memorando Nro.SENESCYT-SAES-DDA-2019-0456-M del 14 septiembre 2019NaNNaNNaNNaN2193517329asigna-acepta_per18.csv
607459112033NaN4158393.07441132.0MASCULINO36456CUBAMestizo240301.0CARLOS ESPINOZA LARREA2403.0SALINAS24.0SANTA ELENA47.0POBLACION GENERAL17124306.0688.01.0OFERTA PÚBLICA61.0UPSEUNIVERSIDAD ESTATAL PENINSULA DE SANTA ELENAUPÚBLICASANTA ELENA1518.0SANTA ELENASANTA ELENASANTA ELENASANTA ELENA21188.0268137.0268137.04748.0ELECTRONICA Y AUTOMATIZACIONINGENIERIA, INDUSTRIA Y CONSTRUCCIONINGENIERIA Y PROFESIONES AFINESPRESENCIALTERCER NIVELVESPERTINA1.01.0100461.043692,62873N1.00.018.0/POBLACION GENERALNIVELACIÓNNO0.0446.0446.0PRIMERA POSTULACIONPRIMERA ASIGNACIÓN DE CUPOS1era Asignación de cupos 1era Postulación P18 de acuerdo a las reglas remitidas en el RF CGTIC-PROY-2019-RF-104 y memorando Nro.SENESCYT-SAES-DDA-2019-0456-M del 14 septiembre 2019NaNNaNNaNNaN2275857429asigna-acepta_per18.csv
607460112034NaN4171155.07466645.0MASCULINO32523CUBANegro130102.012 DE MARZO1301.0PORTOVIEJO13.0MANABI676.0POLITICA DE ACCION AFIRMATIVA17715487.0660.01.0OFERTA PÚBLICA59.0UNEMIUNIVERSIDAD ESTATAL DE MILAGROUPÚBLICAMATRIZ - MILAGRO32.0MILAGROGUAYASMILAGROMILAGRO, CABECERA CANTONAL20840.0273071.0273071.05205.0COMUNICACIONCIENCIAS SOCIALES, PERIODISMO, INFORMACION Y DERECHOPERIODISMO E INFORMACIONEN LINEATERCER NIVELNO APLICA JORNADA1.01.097597.043712,33881N1.00.018.0POLITICA DE ACCION AFIRMATIVANIVELACIÓNNO0.0492.0492.0SEGUNDA POSTULACIÓNTERCERA ASIGNACIÓN DE CUPOS2da Asignación de cupos 2da Postulación P18 de acuerdo a las reglas remitidas en el RF CGTIC-PROY-2019-RF-110 y memorando Nro. SENESCYT-SAES-DDA-2019-0492-M del 03/09/2019NaNNaNNaNNaN2274493665asigna-acepta_per18.csv
607461112035NaN4581450.07901002.0MASCULINO36757CUBAMestizo170121.0LA MAGDALENA1701.0DISTRITO METROPOLITANO DE QUITO17.0PICHINCHA47.0POBLACION GENERAL16671687.0988.01.0OFERTA PÚBLICA46.0UCEUNIVERSIDAD CENTRAL DEL ECUADORUPÚBLICAMATRIZ - QUITO1317.0QUITOPICHINCHADISTRITO METROPOLITANO DE QUITOQUITO DISTRITO METROPOLITANO, CABECERA CANTONAL, CAPITAL PROVINCIAL Y DE LA REPUBLICA DEL ECUADOR20681.0268277.0268277.04462.0MEDICINASALUD Y BIENESTARSALUDPRESENCIALTERCER NIVELINTENSIVA1.01.0104088.043692,27061N1.00.018.0/POBLACION GENERALNIVELACIÓNNO0.0446.0446.0PRIMERA POSTULACIONPRIMERA ASIGNACIÓN DE CUPOS1era Asignación de cupos 1era Postulación P18 de acuerdo a las reglas remitidas en el RF CGTIC-PROY-2019-RF-104 y memorando Nro.SENESCYT-SAES-DDA-2019-0456-M del 14 septiembre 2019NaNNaNNaNNaN1976035865asigna-acepta_per18.csv
607462112036NaN4196646.07517563.0MASCULINO36760CUBAMestizo180103.0HUACHI CHICO1801.0AMBATO18.0TUNGURAHUA47.0POBLACION GENERAL16430065.0907.01.0OFERTA PÚBLICA82.0UTAUNIVERSIDAD TECNICA DE AMBATOUPÚBLICAMATRIZ - AMBATO288.0AMBATOTUNGURAHUAAMBATOLA MERCED19542.0268169.0268169.05205.0COMUNICACIONCIENCIAS SOCIALES, PERIODISMO, INFORMACION Y DERECHOPERIODISMO E INFORMACIONPRESENCIALTERCER NIVELINTENSIVA1.01.0102768.043692,03888N1.00.018.0/POBLACION GENERALNIVELACIÓNNO0.0446.0446.0PRIMERA POSTULACIONPRIMERA ASIGNACIÓN DE CUPOS1era Asignación de cupos 1era Postulación P18 de acuerdo a las reglas remitidas en el RF CGTIC-PROY-2019-RF-104 y memorando Nro.SENESCYT-SAES-DDA-2019-0456-M del 14 septiembre 2019NaNNaNNaNNaN2033736501asigna-acepta_per18.csv
607463112037NaN4010492.07145332.0MASCULINO36857PANAMAAfrodescendiente170112.0IÑAQUITO1701.0DISTRITO METROPOLITANO DE QUITO17.0PICHINCHA676.0POLITICA DE ACCION AFIRMATIVA18402635.0651.03.0OFERTA PÚBLICA22.0ESPEUNIVERSIDAD DE LAS FUERZAS ARMADAS (ESPE)UPÚBLICAEXTENSION - LATACUNGA1330.0LATACUNGACOTOPAXILATACUNGALATACUNGA, CABECERA CANTONAL Y CAPITAL PROVINCIAL20386.0276044.0276044.05247.0PETROQUIMICAINGENIERIA, INDUSTRIA Y CONSTRUCCIONINGENIERIA Y PROFESIONES AFINESPRESENCIALTERCER NIVELINTENSIVA1.01.094303.043726,40104N1.00.018.0POLITICA DE ACCION AFIRMATIVANIVELACIÓNNO0.0494.0494.0TERCERA POSTULACIONQUINTA ASIGNACIÓN DE CUPOS5ta Asignación de cupos 3ra Postulación P18 de acuerdo a las reglas remitidas en el RF CGTIC-PROY-2019-RF-119 y memorando Nro. SENESCYT-SAES-DDA-2019-0521-M del 18-09-2019NaNNaNNaNNaN2155890065asigna-acepta_per18.csv